In [27]:
plt.plot(r.history['loss'],label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
import os
Aquí es donde es necesario cambiar la ruta de trabajo. Se guarda el csv en donde se encuentra la ruta de trabajo antes de ejecutar el codigo.
os.chdir('C:/Users/Scarl/Dropbox/Maestría ITESM/Tesina/Modelos/Definitivos/completo/Revision')
Instalación de paqueterías
#Tensorflow
!pip install tensorflow --trusted-host=pypi.python.org --trusted-host=pypi.org --trusted-host=files.pythonhosted.org
Requirement already satisfied: tensorflow in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (2.9.1) Requirement already satisfied: keras<2.10.0,>=2.9.0rc0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (2.9.0) Requirement already satisfied: keras-preprocessing>=1.1.1 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (1.1.2) Requirement already satisfied: numpy>=1.20 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (1.23.0) Requirement already satisfied: protobuf<3.20,>=3.9.2 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (3.19.4) Requirement already satisfied: tensorflow-estimator<2.10.0,>=2.9.0rc0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (2.9.0) Requirement already satisfied: packaging in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (20.4) Requirement 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google-pasta>=0.1.1 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (0.2.0) Requirement already satisfied: absl-py>=1.0.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (1.1.0) Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (0.4.0) Requirement already satisfied: flatbuffers<2,>=1.12 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (1.12) Requirement already satisfied: termcolor>=1.1.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (1.1.0) Requirement already satisfied: typing-extensions>=3.6.6 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (3.7.4.3) Requirement already satisfied: setuptools in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (50.3.1.post20201107) Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (1.47.0) Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorflow) (0.26.0) Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from astunparse>=1.6.0->tensorflow) (0.35.1) Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.4.6) Requirement already satisfied: google-auth<3,>=1.6.3 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.9.0) Requirement already satisfied: werkzeug>=1.0.1 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (1.0.1) Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.6.1) Requirement already satisfied: markdown>=2.6.8 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (3.3.7) Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (1.8.1) Requirement already satisfied: requests<3,>=2.21.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.24.0) Requirement already satisfied: pyparsing>=2.0.2 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from packaging->tensorflow) (2.4.7) Requirement already satisfied: rsa<5,>=3.1.4 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (4.8) Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (0.2.8) Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (5.2.0) Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (1.3.1) Requirement already satisfied: importlib-metadata>=4.4 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from markdown>=2.6.8->tensorboard<2.10,>=2.9->tensorflow) (4.12.0) Requirement already satisfied: certifi>=2017.4.17 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2020.6.20) Requirement already 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oauthlib>=3.0.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (3.2.0)
WARNING: You are using pip version 22.0.3; however, version 22.3.1 is available. You should consider upgrading via the 'C:\Users\Scarl\OneDrive\Documentos\Anaconda3\python.exe -m pip install --upgrade pip' command.
#Instalar Scikit-learn
!pip install -U scikit-learn --trusted-host=pypi.python.org --trusted-host=pypi.org --trusted-host=files.pythonhosted.org
Requirement already satisfied: scikit-learn in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (1.1.3) Requirement already satisfied: joblib>=1.0.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from scikit-learn) (1.1.0) Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from scikit-learn) (2.1.0) Requirement already satisfied: scipy>=1.3.2 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from scikit-learn) (1.5.2) Requirement already satisfied: numpy>=1.17.3 in c:\users\scarl\onedrive\documentos\anaconda3\lib\site-packages (from scikit-learn) (1.23.0)
WARNING: You are using pip version 22.0.3; however, version 22.3.1 is available. You should consider upgrading via the 'C:\Users\Scarl\OneDrive\Documentos\Anaconda3\python.exe -m pip install --upgrade pip' command.
import tensorflow as tf
print(tf.__version__)
2.9.1
from tensorflow.keras.layers import Input, SimpleRNN, Dense, LSTM, Flatten, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.metrics import mean_squared_error
import math
from tensorflow import keras
from tensorflow.keras import layers
Establecemos una semilla aleatoria
np.random.seed(0)
tf.random.set_seed(0)
#Significado de las letras según sea el caso
# N = number of samples
# T = sequence length
# D = number of input features
# M = number of hideen units
# K = number of output units
Hacemos lectura del archivo csv
df = pd.read_csv('Resumen_petroleo.csv', index_col='Fecha', parse_dates=True)
Transformamos a logaritmo los datos
df['LogMM'] = np.log(df['MM'])
df['LogBRENT'] = np.log(df['BRENT'])
df['LogWTI'] = np.log(df['WTI'])
df['LogOILC1'] = np.log(df['OILC1'])
df['LogDJI'] = np.log(df['DJI'])
df['LogXAU'] = np.log(df['XAU'])
Preparamos los datos que se usaran para entrenamiento así como para pruebas
Ntest = 69
train = df.iloc[:-Ntest] #todos -Ntest
test = df.iloc[-Ntest:] #solo las últimas Ntest observaciones
train_idx = df.index <= train.index[-1] #pone true a lo que sea train del data set y lo demás false en train
test_idx = df.index > train.index[-1] #pone true a lo que sea train del data set y lo demás false en test
Diferenciamos las variables y eliminamos los "NA" generados
df['DiffLogMM'] = df['LogMM'].diff() #crea una nueva columna con la diferencia del logaritmo de los pasajeros
df['DiffLogBRENT'] = df['LogBRENT'].diff()
df['DiffLogWTI'] = df['LogWTI'].diff()
df['DiffLogOILC1'] = df['LogOILC1'].diff()
df['DiffLogDJI'] = df['LogDJI'].diff()
df['DiffLogXAU'] = df['LogXAU'].diff()
DiffLogMM =df['DiffLogMM'].dropna().to_numpy()
DiffLogBRENT = df['DiffLogBRENT'].dropna().to_numpy()
DiffLogWTI = df['DiffLogWTI'].dropna().to_numpy()
DiffLogOILC1 = df['DiffLogOILC1'].dropna().to_numpy()
DiffLogDJI = df['DiffLogDJI'].dropna().to_numpy()
DiffLogXAU = df['DiffLogXAU'].dropna().to_numpy()
series = np.hstack([a.reshape(len(df)-1,-1) for a in [DiffLogMM, DiffLogBRENT, DiffLogWTI, DiffLogOILC1, DiffLogDJI, DiffLogXAU]])
series.shape
(317, 6)
Separamos la base de datos de acuerdo a la estructura seleccionada de rezagos y variables utilizadas.
#Base de datos supervisada
T = 4
X = []
Y = []
for t in range(len(series) - T):
x = series[t:t+T]
X.append(x)
y = series[t+T]
Y.append(y)
X = np.array(X).reshape(-1,T,6)
Y = np.array(Y)
N = len(X)
print("X.shape", X.shape, "Y.shape",Y.shape)
X.shape (313, 4, 6) Y.shape (313, 6)
Asignamos las bases de datos en entrenamiento y pruebas tanto de las variables dependientes como de las independientes
Xtrain, Ytrain = X[: -Ntest], Y[:-Ntest]
Xtest, Ytest = X[-Ntest:], Y[-Ntest:]
XT , YT = Xtest[-T:], Ytest[-T:]
Ytrain = Ytrain[:, 0]
Ytest = Ytest[:, 0]
##--------------- Acá hacemos uno red neuronal bidireccional de prueba---------
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(64, return_sequences=False))(x)
x = layers.Dense(1)(x)
model = Model(i,x)
model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 4, 6)] 0
bidirectional (Bidirectiona (None, 4, 128) 36352
l)
bidirectional_1 (Bidirectio (None, 128) 98816
nal)
dense (Dense) (None, 1) 129
=================================================================
Total params: 135,297
Trainable params: 135,297
Non-trainable params: 0
_________________________________________________________________
model.compile(
loss='mse',
optimizer='adam',
)
r = model.fit(
Xtrain,
Ytrain,
epochs=300,
validation_data=(Xtest,Ytest), verbose = False
)
print("modelo entrenado")
modelo entrenado
Gráfica de los resultados del entrenamiento
plt.plot(r.history['loss'],label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
train_idx[:T+1] = False
Usamos la base de datos para predecir y gráficar los resultados
Ptrain = model.predict(Xtrain).flatten()
Ptest = model.predict(Xtest).flatten()
8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step
#Needed to compute un-differenced predictions
df['ShiftLogMM'] = df['LogMM'].shift(1) #cambia 1 periodo hacia adelante los datos
prev = df['ShiftLogMM']
#último valor del entrenamiento
last_train = train.iloc[-1]['LogMM']
# último valor de la prueba
last_test = test.iloc[-1]['LogMM']
# 1-step forecast[]
df.loc[train_idx, '1step_train'] = prev[train_idx] + Ptrain #Te regresa las n observaciones con true //con el loc me regresa del df algo en específico /
df.loc[test_idx, '1step_test'] = prev[test_idx] + Ptest #Te regresa las n observaciones con true //
#Regreso los datos diferenciados a su fecha orginal
df.loc[test_idx, '1step_test'] = df.loc[test_idx, '1step_test'].shift(-1)
df.loc[train_idx, '1step_train'] = df.loc[train_idx, '1step_train'].shift(-1)
#Al regresar los datos se pierde un dato al final, se añade aquí:
last_forecast_test = prev[test_idx][-2] + Ptest[-1]
df.loc[test_idx, '1step_test'] = df.loc[test_idx, '1step_test'].replace(np.nan, last_forecast_test)
last_forecast_train = prev[train_idx][-2] + Ptrain[-1]
df.loc[train_idx, '1step_train'] = df.loc[train_idx, '1step_train'].replace(np.nan, last_forecast_train)
# Plot 1-step forecast []
df[['LogMM','1step_train','1step_test']].plot(figsize=(15,5));
test_log_pass = df.iloc[-Ntest:]['LogMM']
df.loc[test_idx, '1step_test']
Fecha
2016-10-31 3.758680
2016-11-30 3.679426
2016-12-30 3.844287
2017-01-31 3.810741
2017-02-28 3.842448
...
2022-02-28 4.617223
2022-03-31 4.522333
2022-04-29 4.565668
2022-05-31 4.822040
2022-06-30 4.712503
Name: 1step_test, Length: 69, dtype: float64
MSE = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test']) #test_idx en vez de
RMSE = math.sqrt(MSE)
print("1-step( RMSE:", RMSE)
1-step( RMSE: 0.26285656647696637
#----------Acá termina la prueba, vamos con las redes neuronales:
Tomamos como referencia la base de datos de la sección anterior
Xtrain_m, Ytrain_m = Xtrain, Ytrain
Xtest_m, Ytest_m = Xtest, Ytest
Tx = T
Se hace el diseño de todas las redes neuronales propuestas
import tensorflow as tf
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(2, return_sequences=False)(i)
x = Dense(1)(x)
model2 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(3, return_sequences=False)(i)
x = Dense(1)(x)
model3 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(4, return_sequences=False)(i)
x = Dense(1)(x)
model4 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(5, return_sequences=False)(i)
x = Dense(1)(x)
model5 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(6, return_sequences=False)(i)
x = Dense(1)(x)
model6 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(7, return_sequences=False)(i)
x = Dense(1)(x)
model7 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(8, return_sequences=False)(i)
x = Dense(1)(x)
model8 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(9, return_sequences=False)(i)
x = Dense(1)(x)
model9 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(10, return_sequences=False)(i)
x = Dense(1)(x)
model10 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(11, return_sequences=False)(i)
x = Dense(1)(x)
model11 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(12, return_sequences=False)(i)
x = Dense(1)(x)
model12 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(13, return_sequences=False)(i)
x = Dense(1)(x)
model13 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(14, return_sequences=False)(i)
x = Dense(1)(x)
model14 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(15, return_sequences=False)(i)
x = Dense(1)(x)
model15 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(16, return_sequences=False)(i)
x = Dense(1)(x)
model16 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(17, return_sequences=False)(i)
x = Dense(1)(x)
model17 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(18, return_sequences=False)(i)
x = Dense(1)(x)
model18 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(19, return_sequences=False)(i)
x = Dense(1)(x)
model19 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(20, return_sequences=False)(i)
x = Dense(1)(x)
model20 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(21, return_sequences=False)(i)
x = Dense(1)(x)
model21 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(22, return_sequences=False)(i)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model22 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(23, return_sequences=False)(i)
x = Dense(1)(x)
model23 = Model(i,x)
#RNN multiple LSTM Layers
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(24, return_sequences=False)(i)
x = Dense(1)(x)
model24 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(25, return_sequences=False)(i)
x = Dense(1)(x)
model25 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(26, return_sequences=False)(i)
x = Dense(1)(x)
model26 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(27, return_sequences=False)(i)
x = Dense(1)(x)
model27 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(28, return_sequences=False)(i)
x = Dense(1)(x)
model28 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(29, return_sequences=False)(i)
x = Dense(1)(x)
model29 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(30, return_sequences=False)(i)
x = Dense(1)(x)
model30 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(31, return_sequences=False)(i)
x = Dense(1)(x)
model31 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(32, return_sequences=False)(i)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model32 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(33, return_sequences=False)(i)
x = Dense(1)(x)
model33 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(34, return_sequences=False)(i)
x = Dense(1)(x)
model34 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(35, return_sequences=False)(i)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model35 = Model(i,x)
#RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(36, return_sequences=False)(i)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model36 = Model(i,x)
#Bi-layer RNN
i = Input(shape=(T,6)) #recuerda Tx = T
x = SimpleRNN(2, return_sequences=True)(i) #uno debe ser True y otro False para un dense(1)
x = SimpleRNN(2, return_sequences=False)(x)
x = Dense(1)(x)
model2b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(T,6)) #recuerda Tx = T
x = SimpleRNN(3, return_sequences=True)(i)
x = SimpleRNN(3, return_sequences=False)(x)
x = Dense(1)(x)
model3b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(4, return_sequences=True)(i)
x = SimpleRNN(4, return_sequences=False)(x)
x = Dense(1)(x)
model4b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(5, return_sequences=True)(i)
x = SimpleRNN(5, return_sequences=False)(x)
x = Dense(1)(x)
model5b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(6, return_sequences=True)(i)
x = SimpleRNN(6, return_sequences=False)(x)
x = Dense(1)(x)
model6b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(7, return_sequences=True)(i)
x = SimpleRNN(7, return_sequences=False)(x)
x = Dense(1)(x)
model7b = Model(i,x)
#RBi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(8, return_sequences=True)(i)
x = SimpleRNN(8, return_sequences=False)(x)
x = Dense(1)(x)
model8b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(9, return_sequences=True)(i)
x = SimpleRNN(9, return_sequences=False)(x)
x = Dense(1)(x)
model9b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(10, return_sequences=True)(i)
x = SimpleRNN(10, return_sequences=False)(x)
x = Dense(1)(x)
model10b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(11, return_sequences=True)(i)
x = SimpleRNN(11, return_sequences=False)(x)
x = Dense(1)(x)
model11b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(12, return_sequences=True)(i)
x = SimpleRNN(12, return_sequences=False)(x)
x = Dense(1)(x)
model12b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(13, return_sequences=True)(i)
x = SimpleRNN(13, return_sequences=False)(x)
x = Dense(1)(x)
model13b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(14, return_sequences=True)(i)
x = SimpleRNN(14, return_sequences=False)(x)
x = Dense(1)(x)
model14b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(15, return_sequences=True)(i)
x = SimpleRNN(15, return_sequences=False)(x)
x = Dense(1)(x)
model15b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(16, return_sequences=True)(i)
x = SimpleRNN(16, return_sequences=False)(x)
x = Dense(1)(x)
model16b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(17, return_sequences=True)(i)
x = SimpleRNN(17, return_sequences=False)(x)
x = Dense(1)(x)
model17b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(18, return_sequences=True)(i)
x = SimpleRNN(18, return_sequences=False)(x)
x = Dense(1)(x)
model18b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(19, return_sequences=True)(i)
x = SimpleRNN(19, return_sequences=False)(x)
x = Dense(1)(x)
model19b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(20, return_sequences=True)(i)
x = SimpleRNN(20, return_sequences=False)(x)
x = Dense(1)(x)
model20b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(21, return_sequences=True)(i)
x = SimpleRNN(21, return_sequences=False)(x)
x = Dense(1)(x)
model21b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(22, return_sequences=True)(i)
x = SimpleRNN(22, return_sequences=False)(x)
x = Dense(1)(x)
model22b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(23, return_sequences=True)(i)
x = SimpleRNN(23, return_sequences=False)(x)
x = Dense(1)(x)
model23b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(24, return_sequences=True)(i)
x = SimpleRNN(24, return_sequences=False)(x)
x = Dense(1)(x)
model24b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(25, return_sequences=True)(i)
x = SimpleRNN(25, return_sequences=False)(x)
x = Dense(1)(x)
model25b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(26, return_sequences=True)(i)
x = SimpleRNN(26, return_sequences=False)(x)
x = Dense(1)(x)
model26b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(27, return_sequences=True)(i)
x = SimpleRNN(27, return_sequences=False)(x)
x = Dense(1)(x)
model27b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(28, return_sequences=True)(i)
x = SimpleRNN(28, return_sequences=False)(x)
x = Dense(1)(x)
model28b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(29, return_sequences=True)(i)
x = SimpleRNN(29, return_sequences=False)(x)
x = Dense(1)(x)
model29b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(30, return_sequences=True)(i)
x = SimpleRNN(30, return_sequences=False)(x)
x = Dense(1)(x)
model30b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(31, return_sequences=True)(i)
x = SimpleRNN(31, return_sequences=False)(x)
x = Dense(1)(x)
model31b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(32, return_sequences=True)(i)
x = SimpleRNN(32, return_sequences=False)(x)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model32b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(33, return_sequences=True)(i)
x = SimpleRNN(33, return_sequences=False)(x)
x = Dense(1)(x)
model33b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(34, return_sequences=True)(i)
x = SimpleRNN(34, return_sequences=False)(x)
x = Dense(1)(x)
model34b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(35, return_sequences=True)(i)
x = SimpleRNN(35, return_sequences=False)(x)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model35b = Model(i,x)
#Bi-layer RNN
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = SimpleRNN(36, return_sequences=True)(i)
x = SimpleRNN(36, return_sequences=False)(x)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model36b = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(2)(i)
x = Dense(1)(x)
model2c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(3)(i)
x = Dense(1)(x)
model3c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(4)(i)
x = Dense(1)(x)
model4c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(5)(i)
x = Dense(1)(x)
model5c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(6)(i)
x = Dense(1)(x)
model6c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(7)(i)
x = Dense(1)(x)
model7c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(8)(i)
x = Dense(1)(x)
model8c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(9)(i)
x = Dense(1)(x) #en este caso es 12 =Ty
model9c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(10)(i)
x = Dense(1)(x)
model10c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(11)(i)
x = Dense(1)(x)
model11c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(12)(i)
x = Dense(1)(x)
model12c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(13)(i)
x = Dense(1)(x)
model13c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(14)(i)
x = Dense(1)(x)
model14c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(15)(i)
x = Dense(1)(x)
model15c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(16)(i)
x = Dense(1)(x)
model16c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(17)(i)
x = Dense(1)(x)
model17c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(18)(i)
x = Dense(1)(x)
model18c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(19)(i)
x = Dense(1)(x)
model19c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(20)(i)
x = Dense(1)(x)
model20c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(21)(i)
x = Dense(1)(x)
model21c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(22)(i)
x = Dense(1)(x)
model22c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(23)(i)
x = Dense(1)(x)
model23c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(24)(i)
x = Dense(1)(x)
model24c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(25)(i)
x = Dense(1)(x)
model25c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(26)(i)
x = Dense(1)(x)
model26c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(27)(i)
x = Dense(1)(x)
model27c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(28)(i)
x = Dense(1)(x)
model28c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(29)(i)
x = Dense(1)(x)
model29c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(30)(i)
x = Dense(1)(x)
model30c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(31)(i)
x = Dense(1)(x)
model31c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(32)(i)
x = Dense(1)(x)
model32c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(33)(i)
x = Dense(1)(x)
model33c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(34)(i)
x = Dense(1)(x)
model34c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(35)(i)
x = Dense(1)(x)
model35c = Model(i,x)
#LSTM
i = Input(shape=(Tx,6))
x = LSTM(36)(i)
x = Dense(1)(x)
model36c = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(2, return_sequences=True)(i)
x = LSTM(2, return_sequences=False)(x)
x = Dense(1)(x)
model2d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(3, return_sequences=True)(i)
x = LSTM(3, return_sequences=False)(x)
x = Dense(1)(x)
model3d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(4, return_sequences=True)(i)
x = LSTM(4, return_sequences=False)(x)
x = Dense(1)(x)
model4d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(5, return_sequences=True)(i)
x = LSTM(5, return_sequences=False)(x)
x = Dense(1)(x)
model5d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(6, return_sequences=True)(i)
x = LSTM(6, return_sequences=False)(x)
x = Dense(1)(x)
model6d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(7, return_sequences=True)(i)
x = LSTM(7, return_sequences=False)(x)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model7d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(8, return_sequences=True)(i)
x = LSTM(8, return_sequences=False)(x)
x = Dense(1)(x)
model8d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(9, return_sequences=True)(i)
x = LSTM(9, return_sequences=False)(x)
x = Dense(1)(x)
model9d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(10, return_sequences=True)(i)
x = LSTM(10, return_sequences=False)(x)
x = Dense(1)(x)
model10d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(11, return_sequences=True)(i)
x = LSTM(11, return_sequences=False)(x)
x = Dense(1)(x)
model11d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(12, return_sequences=True)(i)
x = LSTM(12, return_sequences=False)(x)
x = Dense(1)(x)
model12d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(13, return_sequences=True)(i)
x = LSTM(13, return_sequences=False)(x)
x = Dense(1)(x)
model13d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(14, return_sequences=True)(i)
x = LSTM(14, return_sequences=False)(x)
x = Dense(1)(x)
model14d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(15, return_sequences=True)(i)
x = LSTM(15, return_sequences=False)(x)
x = Dense(1)(x)
model15d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(16, return_sequences=True)(i)
x = LSTM(16, return_sequences=False)(x)
x = Dense(1)(x)
model16d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(17, return_sequences=True)(i)
x = LSTM(17, return_sequences=False)(x)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model17d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(18, return_sequences=True)(i)
x = LSTM(18, return_sequences=False)(x)
x = Dense(1)(x)
model18d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(19, return_sequences=True)(i)
x = LSTM(19, return_sequences=False)(x)
x = Dense(1)(x)
model19d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(20, return_sequences=True)(i)
x = LSTM(20, return_sequences=False)(x)
x = Dense(1)(x)
model20d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(21, return_sequences=True)(i)
x = LSTM(21, return_sequences=False)(x)
x = Dense(1)(x)
model21d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(22, return_sequences=True)(i)
x = LSTM(22, return_sequences=False)(x)
x = Dense(1)(x)
model22d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(23, return_sequences=True)(i)
x = LSTM(23, return_sequences=False)(x)
x = Dense(1)(x)
model23d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(24, return_sequences=True)(i)
x = LSTM(24, return_sequences=False)(x)
x = Dense(1)(x)
model24d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(25, return_sequences=True)(i)
x = LSTM(25, return_sequences=False)(x)
x = Dense(1)(x)
model25d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(26, return_sequences=True)(i)
x = LSTM(26, return_sequences=False)(x)
x = Dense(1)(x)
model26d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(27, return_sequences=True)(i)
x = LSTM(27, return_sequences=False)(x)
x = Dense(1)(x)
model27d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(28, return_sequences=True)(i)
x = LSTM(28, return_sequences=False)(x)
x = Dense(1)(x)
model28d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(29, return_sequences=True)(i)
x = LSTM(29, return_sequences=False)(x)
x = Dense(1)(x)
model29d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(30, return_sequences=True)(i)
x = LSTM(30, return_sequences=False)(x)
x = Dense(1)(x)
model30d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(31, return_sequences=True)(i)
x = LSTM(31, return_sequences=False)(x)
x = Dense(1)(x)
model31d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(32, return_sequences=True)(i)
x = LSTM(32, return_sequences=False)(x)
x = Dense(1)(x)
model32d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(33, return_sequences=True)(i)
x = LSTM(33, return_sequences=False)(x)
x = Dense(1)(x)
model33d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(34, return_sequences=True)(i)
x = LSTM(34, return_sequences=False)(x)
x = Dense(1)(x)
model34d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(35, return_sequences=True)(i)
x = LSTM(35, return_sequences=False)(x)
x = Dense(1)(x)
model35d = Model(i,x)
#Bi-layer LSTM
i = Input(shape=(Tx,6)) #recuerda Tx = T
x = LSTM(36, return_sequences=True)(i)
x = LSTM(36, return_sequences=False)(x)
x = Dense(1)(x) #Ty = Ntest el cual es 12 en el ejemplo
model36d = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(2, return_sequences=False))(i)
x = layers.Dense(1)(x)
model2e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(3, return_sequences=False))(i)
x = layers.Dense(1)(x)
model3e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(4, return_sequences=False))(i)
x = layers.Dense(1)(x)
model4e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(5, return_sequences=False))(i)
x = layers.Dense(1)(x)
model5e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(6, return_sequences=False))(i)
x = layers.Dense(1)(x)
model6e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(7, return_sequences=False))(i)
x = layers.Dense(1)(x)
model7e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(8, return_sequences=False))(i)
x = layers.Dense(1)(x)
model8e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(9, return_sequences=False))(i)
x = layers.Dense(1)(x)
model9e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(10, return_sequences=False))(i)
x = layers.Dense(1)(x)
model10e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(11, return_sequences=False))(i)
x = layers.Dense(1)(x)
model11e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(12, return_sequences=False))(i)
x = layers.Dense(1)(x)
model12e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(13, return_sequences=False))(i)
x = layers.Dense(1)(x)
model13e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(14, return_sequences=False))(i)
x = layers.Dense(1)(x)
model14e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(15, return_sequences=False))(i)
x = layers.Dense(1)(x)
model15e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(16, return_sequences=False))(i)
x = layers.Dense(1)(x)
model16e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(17, return_sequences=False))(i)
x = layers.Dense(1)(x)
model17e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(18, return_sequences=False))(i)
x = layers.Dense(1)(x)
model18e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(19, return_sequences=False))(i)
x = layers.Dense(1)(x)
model19e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(20, return_sequences=False))(i)
x = layers.Dense(1)(x)
model20e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(21, return_sequences=False))(i)
x = layers.Dense(1)(x)
model21e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(22, return_sequences=False))(i)
x = layers.Dense(1)(x)
model22e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(23, return_sequences=False))(i)
x = layers.Dense(1)(x)
model23e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(24, return_sequences=False))(i)
x = layers.Dense(1)(x)
model24e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(25, return_sequences=False))(i)
x = layers.Dense(1)(x)
model25e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(26, return_sequences=False))(i)
x = layers.Dense(1)(x)
model26e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(27, return_sequences=False))(i)
x = layers.Dense(1)(x)
model27e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(28, return_sequences=False))(i)
x = layers.Dense(1)(x)
model28e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(29, return_sequences=False))(i)
x = layers.Dense(1)(x)
model29e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(30, return_sequences=False))(i)
x = layers.Dense(1)(x)
model30e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(31, return_sequences=False))(i)
x = layers.Dense(1)(x)
model31e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(32, return_sequences=False))(i)
x = layers.Dense(1)(x)
model32e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(33, return_sequences=False))(i)
x = layers.Dense(1)(x)
model33e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(34, return_sequences=False))(i)
x = layers.Dense(1)(x)
model34e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(35, return_sequences=False))(i)
x = layers.Dense(1)(x)
model35e = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(36, return_sequences=False))(i)
x = layers.Dense(1)(x)
model36e = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(2, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(2, return_sequences=False))(x)
x = layers.Dense(1)(x)
model2f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(3, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(3, return_sequences=False))(x)
x = layers.Dense(1)(x)
model3f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(4, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(4, return_sequences=False))(x)
x = layers.Dense(1)(x)
model4f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(5, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(5, return_sequences=False))(x)
x = layers.Dense(1)(x)
model5f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(6, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(6, return_sequences=False))(x)
x = layers.Dense(1)(x)
model6f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(7, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(7, return_sequences=False))(x)
x = layers.Dense(1)(x)
model7f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(8, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(8, return_sequences=False))(x)
x = layers.Dense(1)(x)
model8f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(9, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(9, return_sequences=False))(x)
x = layers.Dense(1)(x)
model9f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(10, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(10, return_sequences=False))(x)
x = layers.Dense(1)(x)
model10f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(11, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(11, return_sequences=False))(x)
x = layers.Dense(1)(x)
model11f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(12, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(12, return_sequences=False))(x)
x = layers.Dense(1)(x)
model12f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(13, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(13, return_sequences=False))(x)
x = layers.Dense(1)(x)
model13f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(14, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(14, return_sequences=False))(x)
x = layers.Dense(1)(x)
model14f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(15, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(15, return_sequences=False))(x)
x = layers.Dense(1)(x)
model15f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(16, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(16, return_sequences=False))(x)
x = layers.Dense(1)(x)
model16f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(17, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(17, return_sequences=False))(x)
x = layers.Dense(1)(x)
model17f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(18, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(18, return_sequences=False))(x)
x = layers.Dense(1)(x)
model18f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(19, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(19, return_sequences=False))(x)
x = layers.Dense(1)(x)
model19f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(20, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(20, return_sequences=False))(x)
x = layers.Dense(1)(x)
model20f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(21, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(21, return_sequences=False))(x)
x = layers.Dense(1)(x)
model21f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(22, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(22, return_sequences=False))(x)
x = layers.Dense(1)(x)
model22f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(23, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(23, return_sequences=False))(x)
x = layers.Dense(1)(x)
model23f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(24, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(24, return_sequences=False))(x)
x = layers.Dense(1)(x)
model24f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(25, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(25, return_sequences=False))(x)
x = layers.Dense(1)(x)
model25f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(26, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(26, return_sequences=False))(x)
x = layers.Dense(1)(x)
model26f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(27, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(27, return_sequences=False))(x)
x = layers.Dense(1)(x)
model27f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(28, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(28, return_sequences=False))(x)
x = layers.Dense(1)(x)
model28f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(29, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(29, return_sequences=False))(x)
x = layers.Dense(1)(x)
model29f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(30, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(30, return_sequences=False))(x)
x = layers.Dense(1)(x)
model30f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(31, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(31, return_sequences=False))(x)
x = layers.Dense(1)(x)
model31f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(32, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(32, return_sequences=False))(x)
x = layers.Dense(1)(x)
model32f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(33, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(33, return_sequences=False))(x)
x = layers.Dense(1)(x)
model33f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(34, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(34, return_sequences=False))(x)
x = layers.Dense(1)(x)
model34f = Model(i,x)
# Bi-directional Bi-layer LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(35, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(35, return_sequences=False))(x)
x = layers.Dense(1)(x)
model35f = Model(i,x)
# Bi-directional LSTM
i = Input(shape=(T,6))#Son 6 variables de entrada por T = 4 rezagos
x = layers.Bidirectional(layers.LSTM(36, return_sequences=True))(i)
x = layers.Bidirectional(layers.LSTM(36, return_sequences=False))(x)
x = layers.Dense(1)(x)
model36f = Model(i,x)
#Recordar: batch size por defecto es 32 y debe ser menor a las muestras
Se hace un check point solo a forma de control
check_point = ModelCheckpoint(
'best_model.h5',monitor='val_loss',save_best_only=True)
Hacemos la compilación de los modelos, estableciendo el MSE como función de pérdida y al método adam como optimizador.
model2.compile(
loss='mse',
optimizer='adam',
)
model3.compile(
loss='mse',
optimizer='adam',
)
model4.compile(
loss='mse',
optimizer='adam',
)
model5.compile(
loss='mse',
optimizer='adam',
)
model6.compile(
loss='mse',
optimizer='adam',
)
model7.compile(
loss='mse',
optimizer='adam',
)
model8.compile(
loss='mse',
optimizer='adam',
)
model9.compile(
loss='mse',
optimizer='adam',
)
model10.compile(
loss='mse',
optimizer='adam',
)
model11.compile(
loss='mse',
optimizer='adam',
)
model12.compile(
loss='mse',
optimizer='adam',
)
model13.compile(
loss='mse',
optimizer='adam',
)
model14.compile(
loss='mse',
optimizer='adam',
)
model15.compile(
loss='mse',
optimizer='adam',
)
model16.compile(
loss='mse',
optimizer='adam',
)
model17.compile(
loss='mse',
optimizer='adam',
)
model18.compile(
loss='mse',
optimizer='adam',
)
model19.compile(
loss='mse',
optimizer='adam',
)
model20.compile(
loss='mse',
optimizer='adam',
)
model21.compile(
loss='mse',
optimizer='adam',
)
model22.compile(
loss='mse',
optimizer='adam',
)
model23.compile(
loss='mse',
optimizer='adam',
)
model24.compile(
loss='mse',
optimizer='adam',
)
model25.compile(
loss='mse',
optimizer='adam',
)
model26.compile(
loss='mse',
optimizer='adam',
)
model27.compile(
loss='mse',
optimizer='adam',
)
model28.compile(
loss='mse',
optimizer='adam',
)
model29.compile(
loss='mse',
optimizer='adam',
)
model30.compile(
loss='mse',
optimizer='adam',
)
model31.compile(
loss='mse',
optimizer='adam',
)
model32.compile(
loss='mse',
optimizer='adam',
)
model33.compile(
loss='mse',
optimizer='adam',
)
model34.compile(
loss='mse',
optimizer='adam',
)
model35.compile(
loss='mse',
optimizer='adam',
)
model36.compile(
loss='mse',
optimizer='adam',
)
model2b.compile(
loss='mse',
optimizer='adam',
)
model3b.compile(
loss='mse',
optimizer='adam',
)
model4b.compile(
loss='mse',
optimizer='adam',
)
model5b.compile(
loss='mse',
optimizer='adam',
)
model6b.compile(
loss='mse',
optimizer='adam',
)
model7b.compile(
loss='mse',
optimizer='adam',
)
model8b.compile(
loss='mse',
optimizer='adam',
)
model9b.compile(
loss='mse',
optimizer='adam',
)
model10b.compile(
loss='mse',
optimizer='adam',
)
model11b.compile(
loss='mse',
optimizer='adam',
)
model12b.compile(
loss='mse',
optimizer='adam',
)
model13b.compile(
loss='mse',
optimizer='adam',
)
model14b.compile(
loss='mse',
optimizer='adam',
)
model15b.compile(
loss='mse',
optimizer='adam',
)
model16b.compile(
loss='mse',
optimizer='adam',
)
model17b.compile(
loss='mse',
optimizer='adam',
)
model18b.compile(
loss='mse',
optimizer='adam',
)
model19b.compile(
loss='mse',
optimizer='adam',
)
model10b.compile(
loss='mse',
optimizer='adam',
)
model11b.compile(
loss='mse',
optimizer='adam',
)
model12b.compile(
loss='mse',
optimizer='adam',
)
model13b.compile(
loss='mse',
optimizer='adam',
)
model14b.compile(
loss='mse',
optimizer='adam',
)
model15b.compile(
loss='mse',
optimizer='adam',
)
model16b.compile(
loss='mse',
optimizer='adam',
)
model17b.compile(
loss='mse',
optimizer='adam',
)
model18b.compile(
loss='mse',
optimizer='adam',
)
model19b.compile(
loss='mse',
optimizer='adam',
)
model20b.compile(
loss='mse',
optimizer='adam',
)
model21b.compile(
loss='mse',
optimizer='adam',
)
model22b.compile(
loss='mse',
optimizer='adam',
)
model23b.compile(
loss='mse',
optimizer='adam',
)
model24b.compile(
loss='mse',
optimizer='adam',
)
model25b.compile(
loss='mse',
optimizer='adam',
)
model26b.compile(
loss='mse',
optimizer='adam',
)
model27b.compile(
loss='mse',
optimizer='adam',
)
model28b.compile(
loss='mse',
optimizer='adam',
)
model29b.compile(
loss='mse',
optimizer='adam',
)
model30b.compile(
loss='mse',
optimizer='adam',
)
model31b.compile(
loss='mse',
optimizer='adam',
)
model32b.compile(
loss='mse',
optimizer='adam',
)
model33b.compile(
loss='mse',
optimizer='adam',
)
model34b.compile(
loss='mse',
optimizer='adam',
)
model35b.compile(
loss='mse',
optimizer='adam',
)
model36b.compile(
loss='mse',
optimizer='adam',
)
model2c.compile(
loss='mse',
optimizer='adam',
)
model3c.compile(
loss='mse',
optimizer='adam',
)
model4c.compile(
loss='mse',
optimizer='adam',
)
model5c.compile(
loss='mse',
optimizer='adam',
)
model6c.compile(
loss='mse',
optimizer='adam',
)
model7c.compile(
loss='mse',
optimizer='adam',
)
model8c.compile(
loss='mse',
optimizer='adam',
)
model9c.compile(
loss='mse',
optimizer='adam',
)
model10c.compile(
loss='mse',
optimizer='adam',
)
model11c.compile(
loss='mse',
optimizer='adam',
)
model12c.compile(
loss='mse',
optimizer='adam',
)
model13c.compile(
loss='mse',
optimizer='adam',
)
model14c.compile(
loss='mse',
optimizer='adam',
)
model15c.compile(
loss='mse',
optimizer='adam',
)
model16c.compile(
loss='mse',
optimizer='adam',
)
model17c.compile(
loss='mse',
optimizer='adam',
)
model18c.compile(
loss='mse',
optimizer='adam',
)
model19c.compile(
loss='mse',
optimizer='adam',
)
model20c.compile(
loss='mse',
optimizer='adam',
)
model21c.compile(
loss='mse',
optimizer='adam',
)
model22c.compile(
loss='mse',
optimizer='adam',
)
model22c.compile(
loss='mse',
optimizer='adam',
)
model23c.compile(
loss='mse',
optimizer='adam',
)
model24c.compile(
loss='mse',
optimizer='adam',
)
model25c.compile(
loss='mse',
optimizer='adam',
)
model26c.compile(
loss='mse',
optimizer='adam',
)
model27c.compile(
loss='mse',
optimizer='adam',
)
model28c.compile(
loss='mse',
optimizer='adam',
)
model29c.compile(
loss='mse',
optimizer='adam',
)
model30c.compile(
loss='mse',
optimizer='adam',
)
model31c.compile(
loss='mse',
optimizer='adam',
)
model32c.compile(
loss='mse',
optimizer='adam',
)
model33c.compile(
loss='mse',
optimizer='adam',
)
model34c.compile(
loss='mse',
optimizer='adam',
)
model35c.compile(
loss='mse',
optimizer='adam',
)
model36c.compile(
loss='mse',
optimizer='adam',
)
model2d.compile(
loss='mse',
optimizer='adam',
)
model3d.compile(
loss='mse',
optimizer='adam',
)
model4d.compile(
loss='mse',
optimizer='adam',
)
model5d.compile(
loss='mse',
optimizer='adam',
)
model6d.compile(
loss='mse',
optimizer='adam',
)
model7d.compile(
loss='mse',
optimizer='adam',
)
model8d.compile(
loss='mse',
optimizer='adam',
)
model9d.compile(
loss='mse',
optimizer='adam',
)
model10d.compile(
loss='mse',
optimizer='adam',
)
model8d.compile(
loss='mse',
optimizer='adam',
)
model9d.compile(
loss='mse',
optimizer='adam',
)
model10d.compile(
loss='mse',
optimizer='adam',
)
model11d.compile(
loss='mse',
optimizer='adam',
)
model12d.compile(
loss='mse',
optimizer='adam',
)
model13d.compile(
loss='mse',
optimizer='adam',
)
model14d.compile(
loss='mse',
optimizer='adam',
)
model15d.compile(
loss='mse',
optimizer='adam',
)
model16d.compile(
loss='mse',
optimizer='adam',
)
model17d.compile(
loss='mse',
optimizer='adam',
)
model18d.compile(
loss='mse',
optimizer='adam',
)
model19d.compile(
loss='mse',
optimizer='adam',
)
model20d.compile(
loss='mse',
optimizer='adam',
)
model21d.compile(
loss='mse',
optimizer='adam',
)
model22d.compile(
loss='mse',
optimizer='adam',
)
model23d.compile(
loss='mse',
optimizer='adam',
)
model24d.compile(
loss='mse',
optimizer='adam',
)
model25d.compile(
loss='mse',
optimizer='adam',
)
model26d.compile(
loss='mse',
optimizer='adam',
)
model27d.compile(
loss='mse',
optimizer='adam',
)
model28d.compile(
loss='mse',
optimizer='adam',
)
model29d.compile(
loss='mse',
optimizer='adam',
)
model30d.compile(
loss='mse',
optimizer='adam',
)
model31d.compile(
loss='mse',
optimizer='adam',
)
model32d.compile(
loss='mse',
optimizer='adam',
)
model33d.compile(
loss='mse',
optimizer='adam',
)
model34d.compile(
loss='mse',
optimizer='adam',
)
model35d.compile(
loss='mse',
optimizer='adam',
)
model36d.compile(
loss='mse',
optimizer='adam',
)
model2e.compile(
loss='mse',
optimizer='adam',
)
model3e.compile(
loss='mse',
optimizer='adam',
)
model4e.compile(
loss='mse',
optimizer='adam',
)
model5e.compile(
loss='mse',
optimizer='adam',
)
model6e.compile(
loss='mse',
optimizer='adam',
)
model7e.compile(
loss='mse',
optimizer='adam',
)
model8e.compile(
loss='mse',
optimizer='adam',
)
model9e.compile(
loss='mse',
optimizer='adam',
)
model10e.compile(
loss='mse',
optimizer='adam',
)
model11e.compile(
loss='mse',
optimizer='adam',
)
model12e.compile(
loss='mse',
optimizer='adam',
)
model13e.compile(
loss='mse',
optimizer='adam',
)
model14e.compile(
loss='mse',
optimizer='adam',
)
model15e.compile(
loss='mse',
optimizer='adam',
)
model16e.compile(
loss='mse',
optimizer='adam',
)
model17e.compile(
loss='mse',
optimizer='adam',
)
model18e.compile(
loss='mse',
optimizer='adam',
)
model19e.compile(
loss='mse',
optimizer='adam',
)
model20e.compile(
loss='mse',
optimizer='adam',
)
model21e.compile(
loss='mse',
optimizer='adam',
)
model22e.compile(
loss='mse',
optimizer='adam',
)
model23e.compile(
loss='mse',
optimizer='adam',
)
model24e.compile(
loss='mse',
optimizer='adam',
)
model25e.compile(
loss='mse',
optimizer='adam',
)
model26e.compile(
loss='mse',
optimizer='adam',
)
model27e.compile(
loss='mse',
optimizer='adam',
)
model28e.compile(
loss='mse',
optimizer='adam',
)
model29e.compile(
loss='mse',
optimizer='adam',
)
model30e.compile(
loss='mse',
optimizer='adam',
)
model31e.compile(
loss='mse',
optimizer='adam',
)
model32e.compile(
loss='mse',
optimizer='adam',
)
model33e.compile(
loss='mse',
optimizer='adam',
)
model34e.compile(
loss='mse',
optimizer='adam',
)
model35e.compile(
loss='mse',
optimizer='adam',
)
model36e.compile(
loss='mse',
optimizer='adam',
)
model2f.compile(
loss='mse',
optimizer='adam',
)
model3f.compile(
loss='mse',
optimizer='adam',
)
model3f.compile(
loss='mse',
optimizer='adam',
)
model4f.compile(
loss='mse',
optimizer='adam',
)
model5f.compile(
loss='mse',
optimizer='adam',
)
model6f.compile(
loss='mse',
optimizer='adam',
)
model7f.compile(
loss='mse',
optimizer='adam',
)
model8f.compile(
loss='mse',
optimizer='adam',
)
model9f.compile(
loss='mse',
optimizer='adam',
)
model10f.compile(
loss='mse',
optimizer='adam',
)
model11f.compile(
loss='mse',
optimizer='adam',
)
model12f.compile(
loss='mse',
optimizer='adam',
)
model13f.compile(
loss='mse',
optimizer='adam',
)
model14f.compile(
loss='mse',
optimizer='adam',
)
model15f.compile(
loss='mse',
optimizer='adam',
)
model16f.compile(
loss='mse',
optimizer='adam',
)
model17f.compile(
loss='mse',
optimizer='adam',
)
model18f.compile(
loss='mse',
optimizer='adam',
)
model19f.compile(
loss='mse',
optimizer='adam',
)
model20f.compile(
loss='mse',
optimizer='adam',
)
model21f.compile(
loss='mse',
optimizer='adam',
)
model22f.compile(
loss='mse',
optimizer='adam',
)
model23f.compile(
loss='mse',
optimizer='adam',
)
model24f.compile(
loss='mse',
optimizer='adam',
)
model25f.compile(
loss='mse',
optimizer='adam',
)
model26f.compile(
loss='mse',
optimizer='adam',
)
model27f.compile(
loss='mse',
optimizer='adam',
)
model28f.compile(
loss='mse',
optimizer='adam',
)
model29f.compile(
loss='mse',
optimizer='adam',
)
model30f.compile(
loss='mse',
optimizer='adam',
)
model31f.compile(
loss='mse',
optimizer='adam',
)
model32f.compile(
loss='mse',
optimizer='adam',
)
model33f.compile(
loss='mse',
optimizer='adam',
)
model34f.compile(
loss='mse',
optimizer='adam',
)
model35f.compile(
loss='mse',
optimizer='adam',
)
model36f.compile(
loss='mse',
optimizer='adam',
)
r = model2.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m), verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model3.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m), verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model4.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m), verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model5.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model6.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model7.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model8.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model9.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model10.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model11.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model12.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model13.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model14.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model15.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model16.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model17.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model18.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model19.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model20.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model21.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model22.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model23.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model24.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model25.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model26.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model27.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model28.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model29.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model30.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model31.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model32.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model33.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model34.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model35.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model36.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model2b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model3b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model4b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model5b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model6b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model7b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model8b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model9b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model10b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model11b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model12b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model13b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model14b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model15b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model16b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model17b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model18b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model19b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model20b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model21b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model22b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model22b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model23b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model24b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model22b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model23b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model24b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model25b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model26b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model27b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model28b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model29b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model30b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model31b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model32b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model33b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model34b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model35b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model36b.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model2c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model3c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model4c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model5c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model6c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model7c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model8c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model9c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model10c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model11c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model12c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model13c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model14c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model15c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model16c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model17c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model18c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model19c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model20c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model21c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model22c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model23c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model24c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model25c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model26c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model27c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model28c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model29c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model30c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model31c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model32c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model33c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model34c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model35c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model36c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model2d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model3d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model4c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model5c.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model6d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model7d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model8d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model9d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model10d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model11d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model12d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model13d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model14d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model15d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model16d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model17d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model18d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model19d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model20d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model21d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model22d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model23d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model24d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model25d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model26d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model27d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model28d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model29d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model30d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model31d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model32d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model33d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model34d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model35d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model36d.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model2e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model3e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model4e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model5e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model6e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model7e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model8e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model9e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model10e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model11e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model12e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model13e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model14e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model15e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model16e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model17e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model18e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model19e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model20e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model21e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model22e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model23e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model24e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model25e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model26e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model27e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model28e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model29e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model30e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model31e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model32e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model33e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model34e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model35e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model36e.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model2f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model3f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model4f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model5f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model6f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model7f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model8f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model9f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model10f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model11f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model12f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model13f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model14f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model15f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model16f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model17f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model18f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model19f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model20f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model21f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model22f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model23f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model24f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model25f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model26f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model27f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model28f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model29f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model30f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model31f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model32f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model33f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model34f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model35f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
r = model36f.fit(
Xtrain_m,
Ytrain_m,
epochs=300,
validation_data=(Xtest_m,Ytest_m),verbose = False
)
print("modelo entrenado")
#Gráfica
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='test loss')
plt.legend();
modelo entrenado
Usamos los modelos para hacer las predicciones del entrenamiento y pruebas
#Simple RNN 1 Capa
Ptrain = model.predict(Xtrain_m)
Ptest = model.predict(Xtest_m)
Ptrain = Ptrain[:,0]
Ptest = Ptest[0]
Ptrain2 = model2.predict(Xtrain_m)
Ptest2 = model2.predict(Xtest_m)
Ptrain2 = Ptrain2[:,0]
Ptest2 = Ptest2[0]
Ptrain3 = model3.predict(Xtrain_m)
Ptest3 = model3.predict(Xtest_m)
Ptrain3 = Ptrain3[:,0]
Ptest3 = Ptest3[0]
Ptrain4 = model4.predict(Xtrain_m)
Ptest4 = model4.predict(Xtest_m)
Ptrain4 = Ptrain4[:,0]
Ptest4 = Ptest4[0]
Ptrain5 = model5.predict(Xtrain_m)
Ptest5 = model5.predict(Xtest_m)
Ptrain5 = Ptrain5[:,0]
Ptest5 = Ptest5[0]
Ptrain6 = model6.predict(Xtrain_m)
Ptest6 = model6.predict(Xtest_m)
Ptrain6 = Ptrain6[:,0]
Ptest6 = Ptest6[0]
Ptrain7 = model7.predict(Xtrain_m)
Ptest7 = model7.predict(Xtest_m)
Ptrain7 = Ptrain7[:,0]
Ptest7 = Ptest7[0]
Ptrain8 = model8.predict(Xtrain_m)
Ptest8 = model8.predict(Xtest_m)
Ptrain8 = Ptrain8[:,0]
Ptest8 = Ptest8[0]
Ptrain9 = model9.predict(Xtrain_m)
Ptest9 = model9.predict(Xtest_m)
Ptrain9 = Ptrain9[:,0]
Ptest9 = Ptest9[0]
Ptrain10 = model10.predict(Xtrain_m)
Ptest10 = model10.predict(Xtest_m)
Ptrain10 = Ptrain10[:,0]
Ptest10 = Ptest10[0]
Ptrain11 = model11.predict(Xtrain_m)
Ptest11 = model11.predict(Xtest_m)
Ptrain11 = Ptrain11[:,0]
Ptest11 = Ptest11[0]
Ptrain12 = model12.predict(Xtrain_m)
Ptest12 = model12.predict(Xtest_m)
Ptrain12 = Ptrain12[:,0]
Ptest12 = Ptest12[0]
Ptrain13 = model13.predict(Xtrain_m)
Ptest13 = model13.predict(Xtest_m)
Ptrain13 = Ptrain13[:,0]
Ptest13 = Ptest13[0]
Ptrain14 = model14.predict(Xtrain_m)
Ptest14 = model14.predict(Xtest_m)
Ptrain14 = Ptrain14[:,0]
Ptest14 = Ptest14[0]
Ptrain15 = model15.predict(Xtrain_m)
Ptest15 = model15.predict(Xtest_m)
Ptrain15 = Ptrain15[:,0]
Ptest15 = Ptest15[0]
Ptrain16 = model16.predict(Xtrain_m)
Ptest16 = model16.predict(Xtest_m)
Ptrain16 = Ptrain16[:,0]
Ptest16 = Ptest16[0]
Ptrain17 = model17.predict(Xtrain_m)
Ptest17 = model17.predict(Xtest_m)
Ptrain17 = Ptrain17[:,0]
Ptest17 = Ptest17[0]
Ptrain18 = model18.predict(Xtrain_m)
Ptest18 = model18.predict(Xtest_m)
Ptrain18 = Ptrain18[:,0]
Ptest18 = Ptest18[0]
Ptrain19 = model19.predict(Xtrain_m)
Ptest19 = model19.predict(Xtest_m)
Ptrain19 = Ptrain19[:,0]
Ptest19 = Ptest19[0]
Ptrain20 = model20.predict(Xtrain_m)
Ptest20 = model20.predict(Xtest_m)
Ptrain20 = Ptrain20[:,0]
Ptest120 = Ptest20[0]
Ptrain21 = model21.predict(Xtrain_m)
Ptest21 = model21.predict(Xtest_m)
Ptrain21 = Ptrain21[:,0]
Ptest21 = Ptest21[0]
Ptrain22 = model22.predict(Xtrain_m)
Ptest22 = model22.predict(Xtest_m)
Ptrain22 = Ptrain22[:,0]
Ptest22 = Ptest22[0]
Ptrain23 = model23.predict(Xtrain_m)
Ptest23 = model23.predict(Xtest_m)
Ptrain23 = Ptrain23[:,0]
Ptest23 = Ptest23[0]
Ptrain24 = model24.predict(Xtrain_m)
Ptest24 = model24.predict(Xtest_m)
Ptrain24 = Ptrain24[:,0]
Ptest24 = Ptest24[0]
Ptrain25 = model25.predict(Xtrain_m)
Ptest25 = model25.predict(Xtest_m)
Ptrain25 = Ptrain25[:,0]
Ptest25 = Ptest25[0]
Ptrain26 = model26.predict(Xtrain_m)
Ptest26 = model26.predict(Xtest_m)
Ptrain26 = Ptrain26[:,0]
Ptest26 = Ptest26[0]
Ptrain27 = model27.predict(Xtrain_m)
Ptest27 = model27.predict(Xtest_m)
Ptrain27 = Ptrain27[:,0]
Ptest27 = Ptest27[0]
Ptrain28 = model28.predict(Xtrain_m)
Ptest28 = model28.predict(Xtest_m)
Ptrain28 = Ptrain28[:,0]
Ptest28 = Ptest28[0]
Ptrain29 = model29.predict(Xtrain_m)
Ptest29 = model29.predict(Xtest_m)
Ptrain29 = Ptrain29[:,0]
Ptest29 = Ptest29[0]
Ptrain30 = model30.predict(Xtrain_m)
Ptest30 = model30.predict(Xtest_m)
Ptrain30 = Ptrain30[:,0]
Ptest30 = Ptest30[0]
Ptrain31 = model31.predict(Xtrain_m)
Ptest31 = model31.predict(Xtest_m)
Ptrain31 = Ptrain31[:,0]
Ptest31 = Ptest31[0]
Ptrain32 = model32.predict(Xtrain_m)
Ptest32 = model32.predict(Xtest_m)
Ptrain32 = Ptrain32[:,0]
Ptest32 = Ptest32[0]
Ptrain33 = model33.predict(Xtrain_m)
Ptest33 = model33.predict(Xtest_m)
Ptrain33 = Ptrain33[:,0]
Ptest33 = Ptest33[0]
Ptrain34 = model34.predict(Xtrain_m)
Ptest34 = model34.predict(Xtest_m)
Ptrain34 = Ptrain34[:,0]
Ptest34 = Ptest34[0]
Ptrain35 = model35.predict(Xtrain_m)
Ptest35 = model35.predict(Xtest_m)
Ptrain35 = Ptrain35[:,0]
Ptest35 = Ptest35[0]
Ptrain36 = model36.predict(Xtrain_m)
Ptest36 = model36.predict(Xtest_m)
Ptrain36 = Ptrain36[:,0]
Ptest36 = Ptest36[0]
8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 1ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 1ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 1ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 0s/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 0s/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step
# RNN con 2 capas
Ptrain2 = model2.predict(Xtrain_m)
Ptest2 = model2.predict(Xtest_m)
Ptrain2 = Ptrain2[:,0]
Ptest2 = Ptest2[0]
Ptrain3 = model3.predict(Xtrain_m)
Ptest3 = model3.predict(Xtest_m)
Ptrain3 = Ptrain3[:,0]
Ptest3 = Ptest3[0]
Ptrain4 = model4.predict(Xtrain_m)
Ptest4 = model4.predict(Xtest_m)
Ptrain4 = Ptrain4[:,0]
Ptest4 = Ptest4[0]
Ptrain5 = model5.predict(Xtrain_m)
Ptest5 = model5.predict(Xtest_m)
Ptrain5 = Ptrain5[:,0]
Ptest5 = Ptest5[0]
Ptrain6 = model6.predict(Xtrain_m)
Ptest6 = model6.predict(Xtest_m)
Ptrain6 = Ptrain6[:,0]
Ptest6 = Ptest6[0]
Ptrain7 = model7.predict(Xtrain_m)
Ptest7 = model7.predict(Xtest_m)
Ptrain7 = Ptrain7[:,0]
Ptest7 = Ptest7[0]
Ptrain8 = model8.predict(Xtrain_m)
Ptest8 = model8.predict(Xtest_m)
Ptrain8 = Ptrain8[:,0]
Ptest8 = Ptest8[0]
Ptrain9 = model9.predict(Xtrain_m)
Ptest9 = model9.predict(Xtest_m)
Ptrain9 = Ptrain9[:,0]
Ptest9 = Ptest9[0]
Ptrain10 = model10.predict(Xtrain_m)
Ptest10 = model10.predict(Xtest_m)
Ptrain10 = Ptrain10[:,0]
Ptest10 = Ptest10[0]
Ptrain11 = model11.predict(Xtrain_m)
Ptest11 = model11.predict(Xtest_m)
Ptrain11 = Ptrain11[:,0]
Ptest11 = Ptest11[0]
Ptrain12 = model12.predict(Xtrain_m)
Ptest12 = model12.predict(Xtest_m)
Ptrain12 = Ptrain12[:,0]
Ptest12 = Ptest12[0]
Ptrain13 = model13.predict(Xtrain_m)
Ptest13 = model13.predict(Xtest_m)
Ptrain13 = Ptrain13[:,0]
Ptest13 = Ptest13[0]
Ptrain14 = model14.predict(Xtrain_m)
Ptest14 = model14.predict(Xtest_m)
Ptrain14 = Ptrain14[:,0]
Ptest14 = Ptest14[0]
Ptrain15 = model15.predict(Xtrain_m)
Ptest15 = model15.predict(Xtest_m)
Ptrain15 = Ptrain15[:,0]
Ptest15 = Ptest15[0]
Ptrain16 = model16.predict(Xtrain_m)
Ptest16 = model16.predict(Xtest_m)
Ptrain16 = Ptrain16[:,0]
Ptest16 = Ptest16[0]
Ptrain17 = model17.predict(Xtrain_m)
Ptest17 = model17.predict(Xtest_m)
Ptrain17 = Ptrain17[:,0]
Ptest17 = Ptest17[0]
Ptrain18 = model18.predict(Xtrain_m)
Ptest18 = model18.predict(Xtest_m)
Ptrain18 = Ptrain18[:,0]
Ptest18 = Ptest18[0]
Ptrain19 = model19.predict(Xtrain_m)
Ptest19 = model19.predict(Xtest_m)
Ptrain19 = Ptrain19[:,0]
Ptest19 = Ptest19[0]
Ptrain20 = model20.predict(Xtrain_m)
Ptest20 = model20.predict(Xtest_m)
Ptrain20 = Ptrain20[:,0]
Ptest20 = Ptest20[0]
Ptrain21 = model21.predict(Xtrain_m)
Ptest21 = model21.predict(Xtest_m)
Ptrain21 = Ptrain21[:,0]
Ptest21 = Ptest21[0]
Ptrain22 = model22.predict(Xtrain_m)
Ptest22 = model22.predict(Xtest_m)
Ptrain22 = Ptrain22[:,0]
Ptest22 = Ptest22[0]
Ptrain23 = model23.predict(Xtrain_m)
Ptest23 = model23.predict(Xtest_m)
Ptrain23 = Ptrain23[:,0]
Ptest23 = Ptest23[0]
Ptrain24 = model24.predict(Xtrain_m)
Ptest24 = model24.predict(Xtest_m)
Ptrain24 = Ptrain24[:,0]
Ptest24 = Ptest24[0]
Ptrain25 = model25.predict(Xtrain_m)
Ptest25 = model25.predict(Xtest_m)
Ptrain25 = Ptrain25[:,0]
Ptest25 = Ptest25[0]
Ptrain26 = model26.predict(Xtrain_m)
Ptest26 = model26.predict(Xtest_m)
Ptrain26 = Ptrain26[:,0]
Ptest26 = Ptest26[0]
Ptrain27 = model27.predict(Xtrain_m)
Ptest27 = model27.predict(Xtest_m)
Ptrain27 = Ptrain27[:,0]
Ptest27 = Ptest27[0]
Ptrain28 = model28.predict(Xtrain_m)
Ptest28 = model28.predict(Xtest_m)
Ptrain28 = Ptrain28[:,0]
Ptest28 = Ptest28[0]
Ptrain29 = model29.predict(Xtrain_m)
Ptest29 = model29.predict(Xtest_m)
Ptrain29 = Ptrain29[:,0]
Ptest29 = Ptest29[0]
Ptrain30 = model30.predict(Xtrain_m)
Ptest30 = model30.predict(Xtest_m)
Ptrain30 = Ptrain30[:,0]
Ptest30 = Ptest30[0]
Ptrain31 = model31.predict(Xtrain_m)
Ptest31 = model31.predict(Xtest_m)
Ptrain31 = Ptrain31[:,0]
Ptest31 = Ptest31[0]
Ptrain32 = model32.predict(Xtrain_m)
Ptest32 = model32.predict(Xtest_m)
Ptrain32 = Ptrain32[:,0]
Ptest32 = Ptest32[0]
Ptrain33 = model33.predict(Xtrain_m)
Ptest33 = model33.predict(Xtest_m)
Ptrain33 = Ptrain33[:,0]
Ptest33 = Ptest33[0]
Ptrain34 = model34.predict(Xtrain_m)
Ptest34 = model34.predict(Xtest_m)
Ptrain34 = Ptrain34[:,0]
Ptest34 = Ptest34[0]
Ptrain35 = model35.predict(Xtrain_m)
Ptest35 = model35.predict(Xtest_m)
Ptrain35 = Ptrain35[:,0]
Ptest35 = Ptest35[0]
Ptrain36 = model36.predict(Xtrain_m)
Ptest36 = model36.predict(Xtest_m)
Ptrain36 = Ptrain36[:,0]
Ptest36 = Ptest36[0]
Ptrain2b = model2b.predict(Xtrain_m)
Ptest2b = model2b.predict(Xtest_m)
Ptrain2b = Ptrain2b[:,0]
Ptest2b = Ptest2b[0]
Ptrain3b = model3b.predict(Xtrain_m)
Ptest3b = model3b.predict(Xtest_m)
Ptrain3b = Ptrain3b[:,0]
Ptest3b = Ptest3b[0]
Ptrain4b = model4b.predict(Xtrain_m)
Ptest4b = model4b.predict(Xtest_m)
Ptrain4b = Ptrain4b[:,0]
Ptest4b = Ptest4b[0]
Ptrain5b = model5b.predict(Xtrain_m)
Ptest5b = model5b.predict(Xtest_m)
Ptrain5b = Ptrain5b[:,0]
Ptest5b = Ptest5b[0]
Ptrain6b = model6b.predict(Xtrain_m)
Ptest6b = model6b.predict(Xtest_m)
Ptrain6b = Ptrain6b[:,0]
Ptest6b = Ptest6b[0]
Ptrain7b = model7b.predict(Xtrain_m)
Ptest7b = model7b.predict(Xtest_m)
Ptrain7b = Ptrain7b[:,0]
Ptest7b = Ptest7b[0]
Ptrain8b = model8b.predict(Xtrain_m)
Ptest8b = model8b.predict(Xtest_m)
Ptrain8b = Ptrain8b[:,0]
Ptest8b = Ptest8b[0]
Ptrain9b = model9b.predict(Xtrain_m)
Ptest9b = model9b.predict(Xtest_m)
Ptrain9b = Ptrain9b[:,0]
Ptest9b = Ptest9b[0]
Ptrain10b = model10b.predict(Xtrain_m)
Ptest10b = model10b.predict(Xtest_m)
Ptrain10b = Ptrain10b[:,0]
Ptest10b = Ptest10b[0]
Ptrain11b = model11b.predict(Xtrain_m)
Ptest11b = model11b.predict(Xtest_m)
Ptrain11b = Ptrain11b[:,0]
Ptest11b = Ptest11b[0]
Ptrain12b = model12b.predict(Xtrain_m)
Ptest12b = model12b.predict(Xtest_m)
Ptrain12b = Ptrain12b[:,0]
Ptest12b = Ptest12b[0]
Ptrain13b = model13b.predict(Xtrain_m)
Ptest13b = model13b.predict(Xtest_m)
Ptrain13b = Ptrain13b[:,0]
Ptest13b = Ptest13b[0]
Ptrain14b = model14b.predict(Xtrain_m)
Ptest14b = model14b.predict(Xtest_m)
Ptrain14b = Ptrain14b[:,0]
Ptest14b = Ptest14b[0]
Ptrain15b = model15b.predict(Xtrain_m)
Ptest15b = model15b.predict(Xtest_m)
Ptrain15b = Ptrain15b[:,0]
Ptest15b = Ptest15b[0]
Ptrain16b = model16b.predict(Xtrain_m)
Ptest16b = model16b.predict(Xtest_m)
Ptrain16b = Ptrain16b[:,0]
Ptest16b = Ptest16b[0]
Ptrain17b = model17b.predict(Xtrain_m)
Ptest17b = model17b.predict(Xtest_m)
Ptrain17b = Ptrain17b[:,0]
Ptest17b = Ptest17b[0]
Ptrain18b = model18b.predict(Xtrain_m)
Ptest18b = model18b.predict(Xtest_m)
Ptrain18b = Ptrain18b[:,0]
Ptest18b = Ptest18b[0]
Ptrain19b = model19b.predict(Xtrain_m)
Ptest19b = model19b.predict(Xtest_m)
Ptrain19b = Ptrain19b[:,0]
Ptest19b = Ptest19b[0]
Ptrain20b = model20b.predict(Xtrain_m)
Ptest20b = model20b.predict(Xtest_m)
Ptrain20b = Ptrain20b[:,0]
Ptest20b = Ptest20b[0]
Ptrain21b = model21b.predict(Xtrain_m)
Ptest21b = model21b.predict(Xtest_m)
Ptrain21b = Ptrain21b[:,0]
Ptest21b = Ptest21b[0]
Ptrain22b = model22b.predict(Xtrain_m)
Ptest22b = model22b.predict(Xtest_m)
Ptrain22b = Ptrain22b[:,0]
Ptest22b = Ptest22b[0]
Ptrain23b = model23b.predict(Xtrain_m)
Ptest23b = model23b.predict(Xtest_m)
Ptrain23b = Ptrain23b[:,0]
Ptest23b = Ptest23b[0]
Ptrain24b = model24b.predict(Xtrain_m)
Ptest24b = model24b.predict(Xtest_m)
Ptrain24b = Ptrain24b[:,0]
Ptest24b = Ptest24b[0]
Ptrain25b = model25b.predict(Xtrain_m)
Ptest25b = model25b.predict(Xtest_m)
Ptrain25b = Ptrain25b[:,0]
Ptest25b = Ptest25b[0]
Ptrain26b = model26b.predict(Xtrain_m)
Ptest26b = model26b.predict(Xtest_m)
Ptrain26b = Ptrain26b[:,0]
Ptest26b = Ptest26b[0]
Ptrain27b = model27b.predict(Xtrain_m)
Ptest27b = model27b.predict(Xtest_m)
Ptrain27b = Ptrain27b[:,0]
Ptest27b = Ptest27b[0]
Ptrain28b = model28b.predict(Xtrain_m)
Ptest28b = model28b.predict(Xtest_m)
Ptrain28b = Ptrain28b[:,0]
Ptest28b = Ptest28b[0]
Ptrain29b = model29b.predict(Xtrain_m)
Ptest29b = model29b.predict(Xtest_m)
Ptrain29b = Ptrain29b[:,0]
Ptest29b = Ptest29b[0]
Ptrain30b = model30b.predict(Xtrain_m)
Ptest30b = model30b.predict(Xtest_m)
Ptrain30b = Ptrain30b[:,0]
Ptest30b = Ptest30b[0]
Ptrain31b = model31b.predict(Xtrain_m)
Ptest31b = model31b.predict(Xtest_m)
Ptrain31b = Ptrain31b[:,0]
Ptest31b = Ptest31b[0]
Ptrain32b = model32b.predict(Xtrain_m)
Ptest32b = model32b.predict(Xtest_m)
Ptrain32b = Ptrain32b[:,0]
Ptest32b = Ptest32b[0]
Ptrain33b = model33b.predict(Xtrain_m)
Ptest33b = model33b.predict(Xtest_m)
Ptrain33b = Ptrain33b[:,0]
Ptest33b = Ptest33b[0]
Ptrain34b = model34b.predict(Xtrain_m)
Ptest34b = model34b.predict(Xtest_m)
Ptrain34b = Ptrain34b[:,0]
Ptest34b = Ptest34b[0]
Ptrain35b = model35b.predict(Xtrain_m)
Ptest35b = model35b.predict(Xtest_m)
Ptrain35b = Ptrain35b[:,0]
Ptest35b = Ptest35b[0]
Ptrain36b = model36b.predict(Xtrain_m)
Ptest36b = model36b.predict(Xtest_m)
Ptrain36b = Ptrain36b[:,0]
Ptest36b = Ptest36b[0]
8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 1ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 0s/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 1ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step
#LSTM con 1 capa
Ptrain2c = model2c.predict(Xtrain_m)
Ptest2c = model2c.predict(Xtest_m)
Ptrain2c = Ptrain2c[:,0]
Ptest2c = Ptest2c[0]
Ptrain3c = model3c.predict(Xtrain_m)
Ptest3c = model3c.predict(Xtest_m)
Ptrain3c = Ptrain3c[:,0]
Ptest3c = Ptest3c[0]
Ptrain4c = model4c.predict(Xtrain_m)
Ptest4c = model4c.predict(Xtest_m)
Ptrain4c = Ptrain4c[:,0]
Ptest4c = Ptest4c[0]
Ptrain5c = model5c.predict(Xtrain_m)
Ptest5c = model5c.predict(Xtest_m)
Ptrain5c = Ptrain5c[:,0]
Ptest5c = Ptest5c[0]
Ptrain6c = model6c.predict(Xtrain_m)
Ptest6c = model6c.predict(Xtest_m)
Ptrain6c = Ptrain6c[:,0]
Ptest6c = Ptest6c[0]
Ptrain7c = model7c.predict(Xtrain_m)
Ptest7c = model7c.predict(Xtest_m)
Ptrain7c = Ptrain7c[:,0]
Ptest7c = Ptest7c[0]
Ptrain8c = model8c.predict(Xtrain_m)
Ptest8c = model8c.predict(Xtest_m)
Ptrain8c = Ptrain8c[:,0]
Ptest8c = Ptest8c[0]
Ptrain9c = model9c.predict(Xtrain_m)
Ptest9c = model9c.predict(Xtest_m)
Ptrain9c = Ptrain9c[:,0]
Ptest9c = Ptest9c[0]
Ptrain10c = model10c.predict(Xtrain_m)
Ptest10c = model10c.predict(Xtest_m)
Ptrain10c = Ptrain10c[:,0]
Ptest10c = Ptest10c[0]
Ptrain11c = model11c.predict(Xtrain_m)
Ptest11c = model11c.predict(Xtest_m)
Ptrain11c = Ptrain11c[:,0]
Ptest11c = Ptest11c[0]
Ptrain12c = model12c.predict(Xtrain_m)
Ptest12c = model12c.predict(Xtest_m)
Ptrain12c = Ptrain12c[:,0]
Ptest12c = Ptest12c[0]
Ptrain13c = model13c.predict(Xtrain_m)
Ptest13c = model13c.predict(Xtest_m)
Ptrain13c = Ptrain13c[:,0]
Ptest13c = Ptest13c[0]
Ptrain14c = model14c.predict(Xtrain_m)
Ptest14c = model14c.predict(Xtest_m)
Ptrain14c = Ptrain14c[:,0]
Ptest14c = Ptest14c[0]
Ptrain15c = model15c.predict(Xtrain_m)
Ptest15c = model15c.predict(Xtest_m)
Ptrain15c = Ptrain15c[:,0]
Ptest15c = Ptest15c[0]
Ptrain16c = model16c.predict(Xtrain_m)
Ptest16c = model16c.predict(Xtest_m)
Ptrain16c = Ptrain16c[:,0]
Ptest16c = Ptest16c[0]
Ptrain17c = model17c.predict(Xtrain_m)
Ptest17c = model17c.predict(Xtest_m)
Ptrain17c = Ptrain17c[:,0]
Ptest17c = Ptest17c[0]
Ptrain18c = model18c.predict(Xtrain_m)
Ptest18c = model18c.predict(Xtest_m)
Ptrain18c = Ptrain18c[:,0]
Ptest18c = Ptest18c[0]
Ptrain19c = model19c.predict(Xtrain_m)
Ptest19c = model19c.predict(Xtest_m)
Ptrain19c = Ptrain19c[:,0]
Ptest19c = Ptest19c[0]
Ptrain20c = model20c.predict(Xtrain_m)
Ptest20c = model20c.predict(Xtest_m)
Ptrain20c = Ptrain20c[:,0]
Ptest20c = Ptest20c[0]
Ptrain21c = model21c.predict(Xtrain_m)
Ptest21c = model21c.predict(Xtest_m)
Ptrain21c = Ptrain21c[:,0]
Ptest21c = Ptest21c[0]
Ptrain22c = model22c.predict(Xtrain_m)
Ptest22c = model22c.predict(Xtest_m)
Ptrain22c = Ptrain22c[:,0]
Ptest22c = Ptest22c[0]
Ptrain23c = model23c.predict(Xtrain_m)
Ptest23c = model23c.predict(Xtest_m)
Ptrain23c = Ptrain23c[:,0]
Ptest23c = Ptest23c[0]
Ptrain24c = model24c.predict(Xtrain_m)
Ptest24c = model24c.predict(Xtest_m)
Ptrain24c = Ptrain24c[:,0]
Ptest24c = Ptest24c[0]
Ptrain25c = model25c.predict(Xtrain_m)
Ptest25c = model25c.predict(Xtest_m)
Ptrain25c = Ptrain25c[:,0]
Ptest25c = Ptest25c[0]
Ptrain26c = model26c.predict(Xtrain_m)
Ptest26c = model26c.predict(Xtest_m)
Ptrain26c = Ptrain26c[:,0]
Ptest26c = Ptest26c[0]
Ptrain27c = model27c.predict(Xtrain_m)
Ptest27c = model27c.predict(Xtest_m)
Ptrain27c = Ptrain27c[:,0]
Ptest27c = Ptest27c[0]
Ptrain28c = model28c.predict(Xtrain_m)
Ptest28c = model28c.predict(Xtest_m)
Ptrain28c = Ptrain28c[:,0]
Ptest28c = Ptest28c[0]
Ptrain29c = model29c.predict(Xtrain_m)
Ptest29c = model29c.predict(Xtest_m)
Ptrain29c = Ptrain29c[:,0]
Ptest29c = Ptest29c[0]
Ptrain30c = model30c.predict(Xtrain_m)
Ptest30c = model30c.predict(Xtest_m)
Ptrain30c = Ptrain30c[:,0]
Ptest30c = Ptest30c[0]
Ptrain31c = model31c.predict(Xtrain_m)
Ptest31c = model31c.predict(Xtest_m)
Ptrain31c = Ptrain31c[:,0]
Ptest31c = Ptest31c[0]
Ptrain32c = model32c.predict(Xtrain_m)
Ptest32c = model32c.predict(Xtest_m)
Ptrain32c = Ptrain32c[:,0]
Ptest32c = Ptest32c[0]
Ptrain33c = model33c.predict(Xtrain_m)
Ptest33c = model33c.predict(Xtest_m)
Ptrain33c = Ptrain33c[:,0]
Ptest33c = Ptest33c[0]
Ptrain34c = model34c.predict(Xtrain_m)
Ptest34c = model34c.predict(Xtest_m)
Ptrain34c = Ptrain34c[:,0]
Ptest34c = Ptest34c[0]
Ptrain35c = model35c.predict(Xtrain_m)
Ptest35c = model35c.predict(Xtest_m)
Ptrain35c = Ptrain35c[:,0]
Ptest35c = Ptest35c[0]
Ptrain36c = model36c.predict(Xtrain_m)
Ptest36c = model36c.predict(Xtest_m)
Ptrain36c = Ptrain36c[:,0]
Ptest36c = Ptest36c[0]
8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 0s 2ms/step 3/3 [==============================] - 0s 2ms/step
#LSTM con 2 capas
Ptrain2d = model2d.predict(Xtrain_m)
Ptest2d = model2d.predict(Xtest_m)
Ptrain2d = Ptrain2d[:,0]
Ptest2d = Ptest2d[0]
Ptrain3d = model3d.predict(Xtrain_m)
Ptest3d = model3d.predict(Xtest_m)
Ptrain3d = Ptrain3d[:,0]
Ptest3d = Ptest3d[0]
Ptrain4d = model4d.predict(Xtrain_m)
Ptest4d = model4d.predict(Xtest_m)
Ptrain4d = Ptrain4d[:,0]
Ptest4d = Ptest4d[0]
Ptrain5d = model5d.predict(Xtrain_m)
Ptest5d = model5d.predict(Xtest_m)
Ptrain5d = Ptrain5d[:,0]
Ptest5d = Ptest5d[0]
Ptrain6d = model6d.predict(Xtrain_m)
Ptest6d = model6d.predict(Xtest_m)
Ptrain6d = Ptrain6d[:,0]
Ptest6d = Ptest6d[0]
Ptrain7d = model7d.predict(Xtrain_m)
Ptest7d = model7d.predict(Xtest_m)
Ptrain7d = Ptrain7d[:,0]
Ptest7d = Ptest7d[0]
Ptrain8d = model8d.predict(Xtrain_m)
Ptest8d = model8d.predict(Xtest_m)
Ptrain8d = Ptrain8d[:,0]
Ptest8d = Ptest8d[0]
Ptrain9d = model9d.predict(Xtrain_m)
Ptest9d = model9d.predict(Xtest_m)
Ptrain9d = Ptrain9d[:,0]
Ptest9d = Ptest9d[0]
Ptrain10d = model10d.predict(Xtrain_m)
Ptest10d = model10d.predict(Xtest_m)
Ptrain10d = Ptrain10d[:,0]
Ptest10d = Ptest10d[0]
Ptrain11d = model11d.predict(Xtrain_m)
Ptest11d = model11d.predict(Xtest_m)
Ptrain11d = Ptrain11d[:,0]
Ptest11d = Ptest11d[0]
Ptrain12d = model12d.predict(Xtrain_m)
Ptest12d = model12d.predict(Xtest_m)
Ptrain12d = Ptrain12d[:,0]
Ptest12d = Ptest12d[0]
Ptrain13d = model13d.predict(Xtrain_m)
Ptest13d = model13d.predict(Xtest_m)
Ptrain13d = Ptrain13d[:,0]
Ptest13d = Ptest13d[0]
Ptrain14d = model14d.predict(Xtrain_m)
Ptest14d = model14d.predict(Xtest_m)
Ptrain14d = Ptrain14d[:,0]
Ptest14d = Ptest14d[0]
Ptrain15d = model15d.predict(Xtrain_m)
Ptest15d = model15d.predict(Xtest_m)
Ptrain15d = Ptrain15d[:,0]
Ptest15d = Ptest15d[0]
Ptrain16d = model16d.predict(Xtrain_m)
Ptest16d = model16d.predict(Xtest_m)
Ptrain16d = Ptrain16d[:,0]
Ptest16d = Ptest16d[0]
Ptrain17d = model17d.predict(Xtrain_m)
Ptest17d = model17d.predict(Xtest_m)
Ptrain17d = Ptrain17d[:,0]
Ptest17d = Ptest17d[0]
Ptrain18d = model18d.predict(Xtrain_m)
Ptest18d = model18d.predict(Xtest_m)
Ptrain18d = Ptrain18d[:,0]
Ptest18d = Ptest18d[0]
Ptrain19d = model19d.predict(Xtrain_m)
Ptest19d = model19d.predict(Xtest_m)
Ptrain19d = Ptrain19d[:,0]
Ptest19d = Ptest19d[0]
Ptrain20d = model20d.predict(Xtrain_m)
Ptest20d = model20d.predict(Xtest_m)
Ptrain20d = Ptrain20d[:,0]
Ptest20d = Ptest20d[0]
Ptrain21d = model21d.predict(Xtrain_m)
Ptest21d = model21d.predict(Xtest_m)
Ptrain21d = Ptrain21d[:,0]
Ptest21d = Ptest21d[0]
Ptrain22d = model22d.predict(Xtrain_m)
Ptest22d = model22d.predict(Xtest_m)
Ptrain22d = Ptrain22d[:,0]
Ptest22d = Ptest22d[0]
Ptrain23d = model23d.predict(Xtrain_m)
Ptest23d = model23d.predict(Xtest_m)
Ptrain23d = Ptrain23d[:,0]
Ptest23d = Ptest23d[0]
Ptrain24d = model24d.predict(Xtrain_m)
Ptest24d = model24d.predict(Xtest_m)
Ptrain24d = Ptrain24d[:,0]
Ptest24d = Ptest24d[0]
Ptrain25d = model25d.predict(Xtrain_m)
Ptest25d = model25d.predict(Xtest_m)
Ptrain25d = Ptrain25d[:,0]
Ptest25d = Ptest25d[0]
Ptrain26d = model26d.predict(Xtrain_m)
Ptest26d = model26d.predict(Xtest_m)
Ptrain26d = Ptrain26d[:,0]
Ptest26d = Ptest26d[0]
Ptrain27d = model27d.predict(Xtrain_m)
Ptest27d = model27d.predict(Xtest_m)
Ptrain27d = Ptrain27d[:,0]
Ptest27d = Ptest27d[0]
Ptrain28d = model28d.predict(Xtrain_m)
Ptest28d = model28d.predict(Xtest_m)
Ptrain28d = Ptrain28d[:,0]
Ptest28d = Ptest28d[0]
Ptrain29d = model29d.predict(Xtrain_m)
Ptest29d = model29d.predict(Xtest_m)
Ptrain29d = Ptrain29d[:,0]
Ptest29d = Ptest29d[0]
Ptrain30d = model30d.predict(Xtrain_m)
Ptest30d = model30d.predict(Xtest_m)
Ptrain30d = Ptrain30d[:,0]
Ptest30d = Ptest30d[0]
Ptrain31d = model31d.predict(Xtrain_m)
Ptest31d = model31d.predict(Xtest_m)
Ptrain31d = Ptrain31d[:,0]
Ptest31d = Ptest31d[0]
Ptrain32d = model32d.predict(Xtrain_m)
Ptest32d = model32d.predict(Xtest_m)
Ptrain32d = Ptrain32d[:,0]
Ptest32d = Ptest32d[0]
Ptrain33d = model33d.predict(Xtrain_m)
Ptest33d = model33d.predict(Xtest_m)
Ptrain33d = Ptrain33d[:,0]
Ptest33d = Ptest33d[0]
Ptrain34d = model34d.predict(Xtrain_m)
Ptest34d = model34d.predict(Xtest_m)
Ptrain34d = Ptrain34d[:,0]
Ptest34d = Ptest34d[0]
Ptrain35d = model35d.predict(Xtrain_m)
Ptest35d = model35d.predict(Xtest_m)
Ptrain35d = Ptrain35d[:,0]
Ptest35d = Ptest35d[0]
Ptrain36d = model36d.predict(Xtrain_m)
Ptest36d = model36d.predict(Xtest_m)
Ptrain36d = Ptrain36d[:,0]
Ptest36d = Ptest36d[0]
8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 973us/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step
# Bi-directional LSTM con 1 capa
Ptrain2e = model2e.predict(Xtrain_m)
Ptest2e = model2e.predict(Xtest_m)
Ptrain2e = Ptrain2e[:,0]
Ptest2e = Ptest2e[0]
Ptrain3e = model3e.predict(Xtrain_m)
Ptest3e = model3e.predict(Xtest_m)
Ptrain3e = Ptrain3e[:,0]
Ptest3e = Ptest3e[0]
Ptrain4e = model4e.predict(Xtrain_m)
Ptest4e = model4e.predict(Xtest_m)
Ptrain4e = Ptrain4e[:,0]
Ptest4e = Ptest4e[0]
Ptrain5e = model5e.predict(Xtrain_m)
Ptest5e = model5e.predict(Xtest_m)
Ptrain5e = Ptrain5e[:,0]
Ptest5e = Ptest5e[0]
Ptrain6e = model6e.predict(Xtrain_m)
Ptest6e = model6e.predict(Xtest_m)
Ptrain6e = Ptrain6e[:,0]
Ptest6e = Ptest6e[0]
Ptrain7e = model7e.predict(Xtrain_m)
Ptest7e = model7e.predict(Xtest_m)
Ptrain7e = Ptrain7e[:,0]
Ptest7e = Ptest7e[0]
Ptrain8e = model8e.predict(Xtrain_m)
Ptest8e = model8e.predict(Xtest_m)
Ptrain8e = Ptrain8e[:,0]
Ptest8e = Ptest8e[0]
Ptrain9e = model9e.predict(Xtrain_m)
Ptest9e = model9e.predict(Xtest_m)
Ptrain9e = Ptrain9e[:,0]
Ptest9e = Ptest9e[0]
Ptrain10e = model10e.predict(Xtrain_m)
Ptest10e = model10e.predict(Xtest_m)
Ptrain10e = Ptrain10e[:,0]
Ptest10e = Ptest10e[0]
Ptrain11e = model11e.predict(Xtrain_m)
Ptest11e = model11e.predict(Xtest_m)
Ptrain11e = Ptrain11e[:,0]
Ptest11e = Ptest11e[0]
Ptrain12e = model12e.predict(Xtrain_m)
Ptest12e = model12e.predict(Xtest_m)
Ptrain12e = Ptrain12e[:,0]
Ptest12e = Ptest12e[0]
Ptrain13e = model13e.predict(Xtrain_m)
Ptest13e = model13e.predict(Xtest_m)
Ptrain13e = Ptrain13e[:,0]
Ptest13e = Ptest13e[0]
Ptrain14e = model14e.predict(Xtrain_m)
Ptest14e = model14e.predict(Xtest_m)
Ptrain14e = Ptrain14e[:,0]
Ptest14e = Ptest14e[0]
Ptrain15e = model15e.predict(Xtrain_m)
Ptest15e = model15e.predict(Xtest_m)
Ptrain15e = Ptrain15e[:,0]
Ptest15e = Ptest15e[0]
Ptrain16e = model16e.predict(Xtrain_m)
Ptest16e = model16e.predict(Xtest_m)
Ptrain16e = Ptrain16e[:,0]
Ptest16e = Ptest16e[0]
Ptrain17e = model17e.predict(Xtrain_m)
Ptest17e = model17e.predict(Xtest_m)
Ptrain17e = Ptrain17e[:,0]
Ptest17e = Ptest17e[0]
Ptrain18e = model18e.predict(Xtrain_m)
Ptest18e = model18e.predict(Xtest_m)
Ptrain18e = Ptrain18e[:,0]
Ptest18e = Ptest18e[0]
Ptrain19e = model19e.predict(Xtrain_m)
Ptest19e = model19e.predict(Xtest_m)
Ptrain19e = Ptrain19e[:,0]
Ptest19e = Ptest19e[0]
Ptrain20e = model20e.predict(Xtrain_m)
Ptest20e = model20e.predict(Xtest_m)
Ptrain20e = Ptrain20e[:,0]
Ptest20e = Ptest20e[0]
Ptrain21e = model21e.predict(Xtrain_m)
Ptest21e = model21e.predict(Xtest_m)
Ptrain21e = Ptrain21e[:,0]
Ptest21e = Ptest21e[0]
Ptrain22e = model22e.predict(Xtrain_m)
Ptest22e = model22e.predict(Xtest_m)
Ptrain22e = Ptrain22e[:,0]
Ptest22e = Ptest22e[0]
Ptrain23e = model23e.predict(Xtrain_m)
Ptest23e = model23e.predict(Xtest_m)
Ptrain23e = Ptrain23e[:,0]
Ptest23e = Ptest23e[0]
Ptrain24e = model24e.predict(Xtrain_m)
Ptest24e = model24e.predict(Xtest_m)
Ptrain24e = Ptrain24e[:,0]
Ptest24e = Ptest24e[0]
Ptrain25e = model25e.predict(Xtrain_m)
Ptest25e = model25e.predict(Xtest_m)
Ptrain25e = Ptrain25e[:,0]
Ptest25e = Ptest25e[0]
Ptrain26e = model26e.predict(Xtrain_m)
Ptest26e = model26e.predict(Xtest_m)
Ptrain26e = Ptrain26e[:,0]
Ptest26e = Ptest26e[0]
Ptrain27e = model27e.predict(Xtrain_m)
Ptest27e = model27e.predict(Xtest_m)
Ptrain27e = Ptrain27e[:,0]
Ptest27e = Ptest27e[0]
Ptrain28e = model28e.predict(Xtrain_m)
Ptest28e = model28e.predict(Xtest_m)
Ptrain28e = Ptrain28e[:,0]
Ptest28e = Ptest28e[0]
Ptrain29e = model29e.predict(Xtrain_m)
Ptest29e = model29e.predict(Xtest_m)
Ptrain29e = Ptrain29e[:,0]
Ptest29e = Ptest29e[0]
Ptrain30e = model30e.predict(Xtrain_m)
Ptest30e = model30e.predict(Xtest_m)
Ptrain30e = Ptrain30e[:,0]
Ptest30e = Ptest30e[0]
Ptrain31e = model31e.predict(Xtrain_m)
Ptest31e = model31e.predict(Xtest_m)
Ptrain31e = Ptrain31e[:,0]
Ptest31e = Ptest31e[0]
Ptrain32e = model32e.predict(Xtrain_m)
Ptest32e = model32e.predict(Xtest_m)
Ptrain32e = Ptrain32e[:,0]
Ptest32e = Ptest32e[0]
Ptrain33e = model33e.predict(Xtrain_m)
Ptest33e = model33e.predict(Xtest_m)
Ptrain33e = Ptrain33e[:,0]
Ptest33e = Ptest33e[0]
Ptrain34e = model34e.predict(Xtrain_m)
Ptest34e = model34e.predict(Xtest_m)
Ptrain34e = Ptrain34e[:,0]
Ptest34e = Ptest34e[0]
Ptrain35e = model35e.predict(Xtrain_m)
Ptest35e = model35e.predict(Xtest_m)
Ptrain35e = Ptrain35e[:,0]
Ptest35e = Ptest35e[0]
Ptrain36e = model36e.predict(Xtrain_m)
Ptest36e = model36e.predict(Xtest_m)
Ptrain36e = Ptrain36e[:,0]
Ptest36e = Ptest36e[0]
8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 1ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 1ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 1ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 1ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 8ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 1ms/step
#Bi-directional LSTM con 2 capas
Ptrain2f = model2f.predict(Xtrain_m)
Ptest2f = model2f.predict(Xtest_m)
Ptrain2f = Ptrain2f[:,0]
Ptest2f = Ptest2f[0]
Ptrain3f = model3f.predict(Xtrain_m)
Ptest3f = model3f.predict(Xtest_m)
Ptrain3f = Ptrain3f[:,0]
Ptest3f = Ptest3f[0]
Ptrain4f = model4f.predict(Xtrain_m)
Ptest4f = model4f.predict(Xtest_m)
Ptrain4f = Ptrain4f[:,0]
Ptest4f = Ptest4f[0]
Ptrain5f = model5f.predict(Xtrain_m)
Ptest5f = model5f.predict(Xtest_m)
Ptrain5f = Ptrain5f[:,0]
Ptest5f = Ptest5f[0]
Ptrain6f = model6f.predict(Xtrain_m)
Ptest6f = model6f.predict(Xtest_m)
Ptrain6f = Ptrain6f[:,0]
Ptest6f = Ptest6f[0]
Ptrain7f = model7f.predict(Xtrain_m)
Ptest7f = model7f.predict(Xtest_m)
Ptrain7f = Ptrain7f[:,0]
Ptest7f = Ptest7f[0]
Ptrain8f = model8f.predict(Xtrain_m)
Ptest8f = model8f.predict(Xtest_m)
Ptrain8f = Ptrain8f[:,0]
Ptest8f = Ptest8f[0]
Ptrain9f = model9f.predict(Xtrain_m)
Ptest9f = model9f.predict(Xtest_m)
Ptrain9f = Ptrain9f[:,0]
Ptest9f = Ptest9f[0]
Ptrain10f = model10f.predict(Xtrain_m)
Ptest10f = model10f.predict(Xtest_m)
Ptrain10f = Ptrain10f[:,0]
Ptest10f = Ptest10f[0]
Ptrain11f = model11f.predict(Xtrain_m)
Ptest11f = model11f.predict(Xtest_m)
Ptrain11f = Ptrain11f[:,0]
Ptest11f = Ptest11f[0]
Ptrain12f = model12f.predict(Xtrain_m)
Ptest12f = model12f.predict(Xtest_m)
Ptrain12f = Ptrain12f[:,0]
Ptest12f = Ptest12f[0]
Ptrain13f = model13f.predict(Xtrain_m)
Ptest13f = model13f.predict(Xtest_m)
Ptrain13f = Ptrain13f[:,0]
Ptest13f = Ptest13f[0]
Ptrain14f = model14f.predict(Xtrain_m)
Ptest14f = model14f.predict(Xtest_m)
Ptrain14f = Ptrain14f[:,0]
Ptest14f = Ptest14f[0]
Ptrain15f = model15f.predict(Xtrain_m)
Ptest15f = model15f.predict(Xtest_m)
Ptrain15f = Ptrain15f[:,0]
Ptest15f = Ptest15f[0]
Ptrain16f = model16f.predict(Xtrain_m)
Ptest16f = model16f.predict(Xtest_m)
Ptrain16f = Ptrain16f[:,0]
Ptest16f = Ptest16f[0]
Ptrain17f = model17f.predict(Xtrain_m)
Ptest17f = model17f.predict(Xtest_m)
Ptrain17f = Ptrain17f[:,0]
Ptest17f = Ptest17f[0]
Ptrain18f = model18f.predict(Xtrain_m)
Ptest18f = model18f.predict(Xtest_m)
Ptrain18f = Ptrain18f[:,0]
Ptest18f = Ptest18f[0]
Ptrain19f = model19f.predict(Xtrain_m)
Ptest19f = model19f.predict(Xtest_m)
Ptrain19f = Ptrain19f[:,0]
Ptest19f = Ptest19f[0]
Ptrain20f = model20f.predict(Xtrain_m)
Ptest20f = model20f.predict(Xtest_m)
Ptrain20f = Ptrain20f[:,0]
Ptest20f = Ptest20f[0]
Ptrain21f = model21f.predict(Xtrain_m)
Ptest21f = model21f.predict(Xtest_m)
Ptrain21f = Ptrain21f[:,0]
Ptest21f = Ptest21f[0]
Ptrain22f = model22f.predict(Xtrain_m)
Ptest22f = model22f.predict(Xtest_m)
Ptrain22f = Ptrain22f[:,0]
Ptest22f = Ptest22f[0]
Ptrain23f = model23f.predict(Xtrain_m)
Ptest23f = model23f.predict(Xtest_m)
Ptrain23f = Ptrain23f[:,0]
Ptest23f = Ptest23f[0]
Ptrain24f = model24f.predict(Xtrain_m)
Ptest24f = model24f.predict(Xtest_m)
Ptrain24f = Ptrain24f[:,0]
Ptest24f = Ptest24f[0]
Ptrain25f = model25f.predict(Xtrain_m)
Ptest25f = model25f.predict(Xtest_m)
Ptrain25f = Ptrain25f[:,0]
Ptest25f = Ptest25f[0]
Ptrain26f = model26f.predict(Xtrain_m)
Ptest26f = model26f.predict(Xtest_m)
Ptrain26f = Ptrain26f[:,0]
Ptest26f = Ptest26f[0]
Ptrain27f = model27f.predict(Xtrain_m)
Ptest27f = model27f.predict(Xtest_m)
Ptrain27f = Ptrain27f[:,0]
Ptest27f = Ptest27f[0]
Ptrain28f = model28f.predict(Xtrain_m)
Ptest28f = model28f.predict(Xtest_m)
Ptrain28f = Ptrain28f[:,0]
Ptest28f = Ptest28f[0]
Ptrain29f = model29f.predict(Xtrain_m)
Ptest29f = model29f.predict(Xtest_m)
Ptrain29f = Ptrain29f[:,0]
Ptest29f = Ptest29f[0]
Ptrain30f = model30f.predict(Xtrain_m)
Ptest30f = model30f.predict(Xtest_m)
Ptrain30f = Ptrain30f[:,0]
Ptest30f = Ptest30f[0]
Ptrain31f = model31f.predict(Xtrain_m)
Ptest31f = model31f.predict(Xtest_m)
Ptrain31f = Ptrain31f[:,0]
Ptest31f = Ptest31f[0]
Ptrain32f = model32f.predict(Xtrain_m)
Ptest32f = model32f.predict(Xtest_m)
Ptrain32f = Ptrain32f[:,0]
Ptest32f = Ptest32f[0]
Ptrain33f = model33f.predict(Xtrain_m)
Ptest33f = model33f.predict(Xtest_m)
Ptrain33f = Ptrain33f[:,0]
Ptest33f = Ptest33f[0]
Ptrain34f = model34f.predict(Xtrain_m)
Ptest34f = model34f.predict(Xtest_m)
Ptrain34f = Ptrain34f[:,0]
Ptest34f = Ptest34f[0]
Ptrain35f = model35f.predict(Xtrain_m)
Ptest35f = model35f.predict(Xtest_m)
Ptrain35f = Ptrain35f[:,0]
Ptest35f = Ptest35f[0]
Ptrain36f = model36f.predict(Xtrain_m)
Ptest36f = model36f.predict(Xtest_m)
Ptrain36f = Ptrain36f[:,0]
Ptest36f = Ptest36f[0]
8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 1ms/step 3/3 [==============================] - 0s 0s/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 4ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 5ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 2s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 5ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 4ms/step 8/8 [==============================] - 6s 3ms/step 3/3 [==============================] - 0s 4ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 2ms/step 8/8 [==============================] - 1s 4ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 2ms/step 3/3 [==============================] - 0s 3ms/step 8/8 [==============================] - 1s 3ms/step 3/3 [==============================] - 0s 3ms/step
Preparación de datos para el forecast 1-step
# 1-step forecast RNN 1 Capa
df.loc[train_idx, '1step_train2'] = prev[train_idx] + Ptrain3
df.loc[test_idx, '1step_test2'] = prev[test_idx] + Ptest3
df.loc[train_idx, '1step_train3'] = prev[train_idx] + Ptrain3
df.loc[test_idx, '1step_test3'] = prev[test_idx] + Ptest3
df.loc[train_idx, '1step_train4'] = prev[train_idx] + Ptrain4
df.loc[test_idx, '1step_test4'] = prev[test_idx] + Ptest4
df.loc[train_idx, '1step_train5'] = prev[train_idx] + Ptrain5
df.loc[test_idx, '1step_test5'] = prev[test_idx] + Ptest5
df.loc[train_idx, '1step_train6'] = prev[train_idx] + Ptrain6
df.loc[test_idx, '1step_test6'] = prev[test_idx] + Ptest6
df.loc[train_idx, '1step_train7'] = prev[train_idx] + Ptrain7
df.loc[test_idx, '1step_test7'] = prev[test_idx] + Ptest7
df.loc[train_idx, '1step_train8'] = prev[train_idx] + Ptrain8
df.loc[test_idx, '1step_test8'] = prev[test_idx] + Ptest8
df.loc[train_idx, '1step_train9'] = prev[train_idx] + Ptrain9
df.loc[test_idx, '1step_test9'] = prev[test_idx] + Ptest9
df.loc[train_idx, '1step_train10'] = prev[train_idx] + Ptrain10
df.loc[test_idx, '1step_test10'] = prev[test_idx] + Ptest10
df.loc[train_idx, '1step_train11'] = prev[train_idx] + Ptrain11
df.loc[test_idx, '1step_test11'] = prev[test_idx] + Ptest11
df.loc[train_idx, '1step_train12'] = prev[train_idx] + Ptrain12
df.loc[test_idx, '1step_test12'] = prev[test_idx] + Ptest12
df.loc[train_idx, '1step_train13'] = prev[train_idx] + Ptrain13
df.loc[test_idx, '1step_test13'] = prev[test_idx] + Ptest13
df.loc[train_idx, '1step_train14'] = prev[train_idx] + Ptrain14
df.loc[test_idx, '1step_test14'] = prev[test_idx] + Ptest14
df.loc[train_idx, '1step_train15'] = prev[train_idx] + Ptrain15
df.loc[test_idx, '1step_test15'] = prev[test_idx] + Ptest15
df.loc[train_idx, '1step_train16'] = prev[train_idx] + Ptrain16
df.loc[test_idx, '1step_test16'] = prev[test_idx] + Ptest16
df.loc[train_idx, '1step_train17'] = prev[train_idx] + Ptrain17
df.loc[test_idx, '1step_test17'] = prev[test_idx] + Ptest17
df.loc[train_idx, '1step_train18'] = prev[train_idx] + Ptrain18
df.loc[test_idx, '1step_test18'] = prev[test_idx] + Ptest18
df.loc[train_idx, '1step_train19'] = prev[train_idx] + Ptrain19
df.loc[test_idx, '1step_test19'] = prev[test_idx] + Ptest19
df.loc[train_idx, '1step_train20'] = prev[train_idx] + Ptrain20
df.loc[test_idx, '1step_test20'] = prev[test_idx] + Ptest20
df.loc[train_idx, '1step_train21'] = prev[train_idx] + Ptrain21
df.loc[test_idx, '1step_test21'] = prev[test_idx] + Ptest21
df.loc[train_idx, '1step_train22'] = prev[train_idx] + Ptrain22
df.loc[test_idx, '1step_test22'] = prev[test_idx] + Ptest22
df.loc[train_idx, '1step_train23'] = prev[train_idx] + Ptrain23
df.loc[test_idx, '1step_test23'] = prev[test_idx] + Ptest23
df.loc[train_idx, '1step_train24'] = prev[train_idx] + Ptrain24
df.loc[test_idx, '1step_test24'] = prev[test_idx] + Ptest24
df.loc[train_idx, '1step_train25'] = prev[train_idx] + Ptrain25
df.loc[test_idx, '1step_test25'] = prev[test_idx] + Ptest25
df.loc[train_idx, '1step_train26'] = prev[train_idx] + Ptrain26
df.loc[test_idx, '1step_test26'] = prev[test_idx] + Ptest26
df.loc[train_idx, '1step_train27'] = prev[train_idx] + Ptrain27
df.loc[test_idx, '1step_test27'] = prev[test_idx] + Ptest27
df.loc[train_idx, '1step_train28'] = prev[train_idx] + Ptrain28
df.loc[test_idx, '1step_test28'] = prev[test_idx] + Ptest28
df.loc[train_idx, '1step_train29'] = prev[train_idx] + Ptrain29
df.loc[test_idx, '1step_test29'] = prev[test_idx] + Ptest29
df.loc[train_idx, '1step_train30'] = prev[train_idx] + Ptrain30
df.loc[test_idx, '1step_test30'] = prev[test_idx] + Ptest30
df.loc[train_idx, '1step_train31'] = prev[train_idx] + Ptrain31
df.loc[test_idx, '1step_test31'] = prev[test_idx] + Ptest31
df.loc[train_idx, '1step_train32'] = prev[train_idx] + Ptrain32
df.loc[test_idx, '1step_test32'] = prev[test_idx] + Ptest32
df.loc[train_idx, '1step_train33'] = prev[train_idx] + Ptrain33
df.loc[test_idx, '1step_test33'] = prev[test_idx] + Ptest33
df.loc[train_idx, '1step_train34'] = prev[train_idx] + Ptrain34
df.loc[test_idx, '1step_test34'] = prev[test_idx] + Ptest34
df.loc[train_idx, '1step_train35'] = prev[train_idx] + Ptrain35
df.loc[test_idx, '1step_test35'] = prev[test_idx] + Ptest35
df.loc[train_idx, '1step_train36'] = prev[train_idx] + Ptrain36
df.loc[test_idx, '1step_test36'] = prev[test_idx] + Ptest36
# 1-step forecast RNN 2 Capa
df.loc[train_idx, '1step_train2b'] = prev[train_idx] + Ptrain3b
df.loc[test_idx, '1step_test2b'] = prev[test_idx] + Ptest3b
df.loc[train_idx, '1step_train3b'] = prev[train_idx] + Ptrain3b
df.loc[test_idx, '1step_test3b'] = prev[test_idx] + Ptest3b
df.loc[train_idx, '1step_train4b'] = prev[train_idx] + Ptrain4b
df.loc[test_idx, '1step_test4b'] = prev[test_idx] + Ptest4b
df.loc[train_idx, '1step_train5b'] = prev[train_idx] + Ptrain5b
df.loc[test_idx, '1step_test5b'] = prev[test_idx] + Ptest5b
df.loc[train_idx, '1step_train6b'] = prev[train_idx] + Ptrain6b
df.loc[test_idx, '1step_test6b'] = prev[test_idx] + Ptest6b
df.loc[train_idx, '1step_train7b'] = prev[train_idx] + Ptrain7b
df.loc[test_idx, '1step_test7b'] = prev[test_idx] + Ptest7b
df.loc[train_idx, '1step_train8b'] = prev[train_idx] + Ptrain8b
df.loc[test_idx, '1step_test8b'] = prev[test_idx] + Ptest8b
df.loc[train_idx, '1step_train9b'] = prev[train_idx] + Ptrain9b
df.loc[test_idx, '1step_test9b'] = prev[test_idx] + Ptest9b
df.loc[train_idx, '1step_train10b'] = prev[train_idx] + Ptrain10b
df.loc[test_idx, '1step_test10b'] = prev[test_idx] + Ptest10b
df.loc[train_idx, '1step_train11b'] = prev[train_idx] + Ptrain11b
df.loc[test_idx, '1step_test11b'] = prev[test_idx] + Ptest11b
df.loc[train_idx, '1step_train12b'] = prev[train_idx] + Ptrain12b
df.loc[test_idx, '1step_test12b'] = prev[test_idx] + Ptest12b
df.loc[train_idx, '1step_train13b'] = prev[train_idx] + Ptrain13b
df.loc[test_idx, '1step_test13b'] = prev[test_idx] + Ptest13b
df.loc[train_idx, '1step_train14b'] = prev[train_idx] + Ptrain14b
df.loc[test_idx, '1step_test14b'] = prev[test_idx] + Ptest14b
df.loc[train_idx, '1step_train15b'] = prev[train_idx] + Ptrain15b
df.loc[test_idx, '1step_test15b'] = prev[test_idx] + Ptest15b
df.loc[train_idx, '1step_train16b'] = prev[train_idx] + Ptrain16b
df.loc[test_idx, '1step_test16b'] = prev[test_idx] + Ptest16b
df.loc[train_idx, '1step_train17b'] = prev[train_idx] + Ptrain17b
df.loc[test_idx, '1step_test17b'] = prev[test_idx] + Ptest17b
df.loc[train_idx, '1step_train18b'] = prev[train_idx] + Ptrain18b
df.loc[test_idx, '1step_test18b'] = prev[test_idx] + Ptest18b
df.loc[train_idx, '1step_train19b'] = prev[train_idx] + Ptrain19b
df.loc[test_idx, '1step_test19b'] = prev[test_idx] + Ptest19b
df.loc[train_idx, '1step_train20b'] = prev[train_idx] + Ptrain20b
df.loc[test_idx, '1step_test20b'] = prev[test_idx] + Ptest20b
df.loc[train_idx, '1step_train21b'] = prev[train_idx] + Ptrain21b
df.loc[test_idx, '1step_test21b'] = prev[test_idx] + Ptest21b
df.loc[train_idx, '1step_train22b'] = prev[train_idx] + Ptrain22b
df.loc[test_idx, '1step_test22b'] = prev[test_idx] + Ptest22b
df.loc[train_idx, '1step_train23b'] = prev[train_idx] + Ptrain23b
df.loc[test_idx, '1step_test23b'] = prev[test_idx] + Ptest23b
df.loc[train_idx, '1step_train24b'] = prev[train_idx] + Ptrain24b
df.loc[test_idx, '1step_test24b'] = prev[test_idx] + Ptest24b
df.loc[train_idx, '1step_train25b'] = prev[train_idx] + Ptrain25b
df.loc[test_idx, '1step_test25b'] = prev[test_idx] + Ptest25b
df.loc[train_idx, '1step_train26b'] = prev[train_idx] + Ptrain26b
df.loc[test_idx, '1step_test26b'] = prev[test_idx] + Ptest26b
df.loc[train_idx, '1step_train27b'] = prev[train_idx] + Ptrain27b
df.loc[test_idx, '1step_test27b'] = prev[test_idx] + Ptest27b
df.loc[train_idx, '1step_train28b'] = prev[train_idx] + Ptrain28b
df.loc[test_idx, '1step_test28b'] = prev[test_idx] + Ptest28b
df.loc[train_idx, '1step_train29b'] = prev[train_idx] + Ptrain29b
df.loc[test_idx, '1step_test29b'] = prev[test_idx] + Ptest29b
df.loc[train_idx, '1step_train30b'] = prev[train_idx] + Ptrain30b
df.loc[test_idx, '1step_test30b'] = prev[test_idx] + Ptest30b
df.loc[train_idx, '1step_train31b'] = prev[train_idx] + Ptrain31b
df.loc[test_idx, '1step_test31b'] = prev[test_idx] + Ptest31b
df.loc[train_idx, '1step_train32b'] = prev[train_idx] + Ptrain32b
df.loc[test_idx, '1step_test32b'] = prev[test_idx] + Ptest32b
df.loc[train_idx, '1step_train33b'] = prev[train_idx] + Ptrain33b
df.loc[test_idx, '1step_test33b'] = prev[test_idx] + Ptest33b
df.loc[train_idx, '1step_train34b'] = prev[train_idx] + Ptrain34b
df.loc[test_idx, '1step_test34b'] = prev[test_idx] + Ptest34b
df.loc[train_idx, '1step_train35b'] = prev[train_idx] + Ptrain35b
df.loc[test_idx, '1step_test35b'] = prev[test_idx] + Ptest35b
df.loc[train_idx, '1step_train36b'] = prev[train_idx] + Ptrain36b
df.loc[test_idx, '1step_test36b'] = prev[test_idx] + Ptest36b
# 1-step forecast LSTM 1 Capa
df.loc[train_idx, '1step_train2c'] = prev[train_idx] + Ptrain3c
df.loc[test_idx, '1step_test2c'] = prev[test_idx] + Ptest3c
df.loc[train_idx, '1step_train3c'] = prev[train_idx] + Ptrain3c
df.loc[test_idx, '1step_test3c'] = prev[test_idx] + Ptest3c
df.loc[train_idx, '1step_train4c'] = prev[train_idx] + Ptrain4c
df.loc[test_idx, '1step_test4c'] = prev[test_idx] + Ptest4c
df.loc[train_idx, '1step_train5c'] = prev[train_idx] + Ptrain5c
df.loc[test_idx, '1step_test5c'] = prev[test_idx] + Ptest5c
df.loc[train_idx, '1step_train6c'] = prev[train_idx] + Ptrain6c
df.loc[test_idx, '1step_test6c'] = prev[test_idx] + Ptest6c
df.loc[train_idx, '1step_train7c'] = prev[train_idx] + Ptrain7c
df.loc[test_idx, '1step_test7c'] = prev[test_idx] + Ptest7c
df.loc[train_idx, '1step_train8c'] = prev[train_idx] + Ptrain8c
df.loc[test_idx, '1step_test8c'] = prev[test_idx] + Ptest8c
df.loc[train_idx, '1step_train9c'] = prev[train_idx] + Ptrain9c
df.loc[test_idx, '1step_test9c'] = prev[test_idx] + Ptest9c
df.loc[train_idx, '1step_train10c'] = prev[train_idx] + Ptrain10c
df.loc[test_idx, '1step_test10c'] = prev[test_idx] + Ptest10c
df.loc[train_idx, '1step_train11c'] = prev[train_idx] + Ptrain11c
df.loc[test_idx, '1step_test11c'] = prev[test_idx] + Ptest11c
df.loc[train_idx, '1step_train12c'] = prev[train_idx] + Ptrain12c
df.loc[test_idx, '1step_test12c'] = prev[test_idx] + Ptest12c
df.loc[train_idx, '1step_train13c'] = prev[train_idx] + Ptrain13c
df.loc[test_idx, '1step_test13c'] = prev[test_idx] + Ptest13c
df.loc[train_idx, '1step_train14c'] = prev[train_idx] + Ptrain14c
df.loc[test_idx, '1step_test14c'] = prev[test_idx] + Ptest14c
df.loc[train_idx, '1step_train15c'] = prev[train_idx] + Ptrain15c
df.loc[test_idx, '1step_test15c'] = prev[test_idx] + Ptest15c
df.loc[train_idx, '1step_train16c'] = prev[train_idx] + Ptrain16c
df.loc[test_idx, '1step_test16c'] = prev[test_idx] + Ptest16c
df.loc[train_idx, '1step_train17c'] = prev[train_idx] + Ptrain17c
df.loc[test_idx, '1step_test17c'] = prev[test_idx] + Ptest17c
df.loc[train_idx, '1step_train18c'] = prev[train_idx] + Ptrain18c
df.loc[test_idx, '1step_test18c'] = prev[test_idx] + Ptest18c
df.loc[train_idx, '1step_train19c'] = prev[train_idx] + Ptrain19c
df.loc[test_idx, '1step_test19c'] = prev[test_idx] + Ptest19c
df.loc[train_idx, '1step_train20c'] = prev[train_idx] + Ptrain20c
df.loc[test_idx, '1step_test20c'] = prev[test_idx] + Ptest20c
df.loc[train_idx, '1step_train21c'] = prev[train_idx] + Ptrain21c
df.loc[test_idx, '1step_test21c'] = prev[test_idx] + Ptest21c
df.loc[train_idx, '1step_train22c'] = prev[train_idx] + Ptrain22c
df.loc[test_idx, '1step_test22c'] = prev[test_idx] + Ptest22c
df.loc[train_idx, '1step_train23c'] = prev[train_idx] + Ptrain23c
df.loc[test_idx, '1step_test23c'] = prev[test_idx] + Ptest23c
df.loc[train_idx, '1step_train24c'] = prev[train_idx] + Ptrain24c
df.loc[test_idx, '1step_test24c'] = prev[test_idx] + Ptest24c
df.loc[train_idx, '1step_train25c'] = prev[train_idx] + Ptrain25c
df.loc[test_idx, '1step_test25c'] = prev[test_idx] + Ptest25c
df.loc[train_idx, '1step_train26c'] = prev[train_idx] + Ptrain26c
df.loc[test_idx, '1step_test26c'] = prev[test_idx] + Ptest26c
df.loc[train_idx, '1step_train27c'] = prev[train_idx] + Ptrain27c
df.loc[test_idx, '1step_test27c'] = prev[test_idx] + Ptest27c
df.loc[train_idx, '1step_train28c'] = prev[train_idx] + Ptrain28c
df.loc[test_idx, '1step_test28c'] = prev[test_idx] + Ptest28c
df.loc[train_idx, '1step_train29c'] = prev[train_idx] + Ptrain29c
df.loc[test_idx, '1step_test29c'] = prev[test_idx] + Ptest29c
df.loc[train_idx, '1step_train30c'] = prev[train_idx] + Ptrain30c
df.loc[test_idx, '1step_test30c'] = prev[test_idx] + Ptest30c
df.loc[train_idx, '1step_train31c'] = prev[train_idx] + Ptrain31c
df.loc[test_idx, '1step_test31c'] = prev[test_idx] + Ptest31c
df.loc[train_idx, '1step_train32c'] = prev[train_idx] + Ptrain32c
df.loc[test_idx, '1step_test32c'] = prev[test_idx] + Ptest32c
df.loc[train_idx, '1step_train33c'] = prev[train_idx] + Ptrain33c
df.loc[test_idx, '1step_test33c'] = prev[test_idx] + Ptest33c
df.loc[train_idx, '1step_train34c'] = prev[train_idx] + Ptrain34c
df.loc[test_idx, '1step_test34c'] = prev[test_idx] + Ptest34c
df.loc[train_idx, '1step_train35c'] = prev[train_idx] + Ptrain35c
df.loc[test_idx, '1step_test35c'] = prev[test_idx] + Ptest35c
df.loc[train_idx, '1step_train36c'] = prev[train_idx] + Ptrain36c
df.loc[test_idx, '1step_test36c'] = prev[test_idx] + Ptest36c
# 1-step forecast LSTM 2 Capa
df.loc[train_idx, '1step_train2d'] = prev[train_idx] + Ptrain3d
df.loc[test_idx, '1step_test2d'] = prev[test_idx] + Ptest3d
df.loc[train_idx, '1step_train3d'] = prev[train_idx] + Ptrain3d
df.loc[test_idx, '1step_test3d'] = prev[test_idx] + Ptest3d
df.loc[train_idx, '1step_train4d'] = prev[train_idx] + Ptrain4d
df.loc[test_idx, '1step_test4d'] = prev[test_idx] + Ptest4d
df.loc[train_idx, '1step_train5d'] = prev[train_idx] + Ptrain5d
df.loc[test_idx, '1step_test5d'] = prev[test_idx] + Ptest5d
df.loc[train_idx, '1step_train6d'] = prev[train_idx] + Ptrain6d
df.loc[test_idx, '1step_test6d'] = prev[test_idx] + Ptest6d
df.loc[train_idx, '1step_train7d'] = prev[train_idx] + Ptrain7d
df.loc[test_idx, '1step_test7d'] = prev[test_idx] + Ptest7d
df.loc[train_idx, '1step_train8d'] = prev[train_idx] + Ptrain8d
df.loc[test_idx, '1step_test8d'] = prev[test_idx] + Ptest8d
df.loc[train_idx, '1step_train9d'] = prev[train_idx] + Ptrain9d
df.loc[test_idx, '1step_test9d'] = prev[test_idx] + Ptest9d
df.loc[train_idx, '1step_train10d'] = prev[train_idx] + Ptrain10d
df.loc[test_idx, '1step_test10d'] = prev[test_idx] + Ptest10d
df.loc[train_idx, '1step_train11d'] = prev[train_idx] + Ptrain11d
df.loc[test_idx, '1step_test11d'] = prev[test_idx] + Ptest11d
df.loc[train_idx, '1step_train12d'] = prev[train_idx] + Ptrain12d
df.loc[test_idx, '1step_test12d'] = prev[test_idx] + Ptest12d
df.loc[train_idx, '1step_train13d'] = prev[train_idx] + Ptrain13d
df.loc[test_idx, '1step_test13d'] = prev[test_idx] + Ptest13d
df.loc[train_idx, '1step_train14d'] = prev[train_idx] + Ptrain14d
df.loc[test_idx, '1step_test14d'] = prev[test_idx] + Ptest14d
df.loc[train_idx, '1step_train15d'] = prev[train_idx] + Ptrain15d
df.loc[test_idx, '1step_test15d'] = prev[test_idx] + Ptest15d
df.loc[train_idx, '1step_train16d'] = prev[train_idx] + Ptrain16d
df.loc[test_idx, '1step_test16d'] = prev[test_idx] + Ptest16d
df.loc[train_idx, '1step_train17d'] = prev[train_idx] + Ptrain17d
df.loc[test_idx, '1step_test17d'] = prev[test_idx] + Ptest17d
df.loc[train_idx, '1step_train18d'] = prev[train_idx] + Ptrain18d
df.loc[test_idx, '1step_test18d'] = prev[test_idx] + Ptest18d
df.loc[train_idx, '1step_train19d'] = prev[train_idx] + Ptrain19d
df.loc[test_idx, '1step_test19d'] = prev[test_idx] + Ptest19d
df.loc[train_idx, '1step_train20d'] = prev[train_idx] + Ptrain20d
df.loc[test_idx, '1step_test20d'] = prev[test_idx] + Ptest20d
df.loc[train_idx, '1step_train21d'] = prev[train_idx] + Ptrain21d
df.loc[test_idx, '1step_test21d'] = prev[test_idx] + Ptest21d
df.loc[train_idx, '1step_train22d'] = prev[train_idx] + Ptrain22d
df.loc[test_idx, '1step_test22d'] = prev[test_idx] + Ptest22d
df.loc[train_idx, '1step_train23d'] = prev[train_idx] + Ptrain23d
df.loc[test_idx, '1step_test23d'] = prev[test_idx] + Ptest23d
df.loc[train_idx, '1step_train24d'] = prev[train_idx] + Ptrain24d
df.loc[test_idx, '1step_test24d'] = prev[test_idx] + Ptest24d
df.loc[train_idx, '1step_train25d'] = prev[train_idx] + Ptrain25d
df.loc[test_idx, '1step_test25d'] = prev[test_idx] + Ptest25d
df.loc[train_idx, '1step_train26d'] = prev[train_idx] + Ptrain26d
df.loc[test_idx, '1step_test26d'] = prev[test_idx] + Ptest26d
df.loc[train_idx, '1step_train27d'] = prev[train_idx] + Ptrain27d
df.loc[test_idx, '1step_test27d'] = prev[test_idx] + Ptest27d
df.loc[train_idx, '1step_train28d'] = prev[train_idx] + Ptrain28d
df.loc[test_idx, '1step_test28d'] = prev[test_idx] + Ptest28d
df.loc[train_idx, '1step_train29d'] = prev[train_idx] + Ptrain29d
df.loc[test_idx, '1step_test29d'] = prev[test_idx] + Ptest29d
df.loc[train_idx, '1step_train30d'] = prev[train_idx] + Ptrain30d
df.loc[test_idx, '1step_test30d'] = prev[test_idx] + Ptest30d
df.loc[train_idx, '1step_train31d'] = prev[train_idx] + Ptrain31d
df.loc[test_idx, '1step_test31d'] = prev[test_idx] + Ptest31d
df.loc[train_idx, '1step_train32d'] = prev[train_idx] + Ptrain32d
df.loc[test_idx, '1step_test32d'] = prev[test_idx] + Ptest32d
df.loc[train_idx, '1step_train33d'] = prev[train_idx] + Ptrain33d
df.loc[test_idx, '1step_test33d'] = prev[test_idx] + Ptest33d
df.loc[train_idx, '1step_train34d'] = prev[train_idx] + Ptrain34d
df.loc[test_idx, '1step_test34d'] = prev[test_idx] + Ptest34d
df.loc[train_idx, '1step_train35d'] = prev[train_idx] + Ptrain35d
df.loc[test_idx, '1step_test35d'] = prev[test_idx] + Ptest35d
df.loc[train_idx, '1step_train36d'] = prev[train_idx] + Ptrain36d
df.loc[test_idx, '1step_test36d'] = prev[test_idx] + Ptest36d
# 1-step forecast Bi-LSTM 1 Capa
df.loc[train_idx, '1step_train2e'] = prev[train_idx] + Ptrain3e
df.loc[test_idx, '1step_test2e'] = prev[test_idx] + Ptest3e
df.loc[train_idx, '1step_train3e'] = prev[train_idx] + Ptrain3e
df.loc[test_idx, '1step_test3e'] = prev[test_idx] + Ptest3e
df.loc[train_idx, '1step_train4e'] = prev[train_idx] + Ptrain4e
df.loc[test_idx, '1step_test4e'] = prev[test_idx] + Ptest4e
df.loc[train_idx, '1step_train5e'] = prev[train_idx] + Ptrain5e
df.loc[test_idx, '1step_test5e'] = prev[test_idx] + Ptest5e
df.loc[train_idx, '1step_train6e'] = prev[train_idx] + Ptrain6e
df.loc[test_idx, '1step_test6e'] = prev[test_idx] + Ptest6e
df.loc[train_idx, '1step_train7e'] = prev[train_idx] + Ptrain7e
df.loc[test_idx, '1step_test7e'] = prev[test_idx] + Ptest7e
df.loc[train_idx, '1step_train8e'] = prev[train_idx] + Ptrain8e
df.loc[test_idx, '1step_test8e'] = prev[test_idx] + Ptest8e
df.loc[train_idx, '1step_train9e'] = prev[train_idx] + Ptrain9e
df.loc[test_idx, '1step_test9e'] = prev[test_idx] + Ptest9e
df.loc[train_idx, '1step_train10e'] = prev[train_idx] + Ptrain10e
df.loc[test_idx, '1step_test10e'] = prev[test_idx] + Ptest10e
df.loc[train_idx, '1step_train11e'] = prev[train_idx] + Ptrain11e
df.loc[test_idx, '1step_test11e'] = prev[test_idx] + Ptest11e
df.loc[train_idx, '1step_train12e'] = prev[train_idx] + Ptrain12e
df.loc[test_idx, '1step_test12e'] = prev[test_idx] + Ptest12e
df.loc[train_idx, '1step_train13e'] = prev[train_idx] + Ptrain13e
df.loc[test_idx, '1step_test13e'] = prev[test_idx] + Ptest13e
df.loc[train_idx, '1step_train14e'] = prev[train_idx] + Ptrain14e
df.loc[test_idx, '1step_test14e'] = prev[test_idx] + Ptest14e
df.loc[train_idx, '1step_train15e'] = prev[train_idx] + Ptrain15e
df.loc[test_idx, '1step_test15e'] = prev[test_idx] + Ptest15e
df.loc[train_idx, '1step_train16e'] = prev[train_idx] + Ptrain16e
df.loc[test_idx, '1step_test16e'] = prev[test_idx] + Ptest16e
df.loc[train_idx, '1step_train17e'] = prev[train_idx] + Ptrain17e
df.loc[test_idx, '1step_test17e'] = prev[test_idx] + Ptest17e
df.loc[train_idx, '1step_train18e'] = prev[train_idx] + Ptrain18e
df.loc[test_idx, '1step_test18e'] = prev[test_idx] + Ptest18e
df.loc[train_idx, '1step_train19e'] = prev[train_idx] + Ptrain19e
df.loc[test_idx, '1step_test19e'] = prev[test_idx] + Ptest19e
df.loc[train_idx, '1step_train20e'] = prev[train_idx] + Ptrain20e
df.loc[test_idx, '1step_test20e'] = prev[test_idx] + Ptest20e
df.loc[train_idx, '1step_train21e'] = prev[train_idx] + Ptrain21e
df.loc[test_idx, '1step_test21e'] = prev[test_idx] + Ptest21e
df.loc[train_idx, '1step_train22e'] = prev[train_idx] + Ptrain22e
df.loc[test_idx, '1step_test22e'] = prev[test_idx] + Ptest22e
df.loc[train_idx, '1step_train23e'] = prev[train_idx] + Ptrain23e
df.loc[test_idx, '1step_test23e'] = prev[test_idx] + Ptest23e
df.loc[train_idx, '1step_train24e'] = prev[train_idx] + Ptrain24e
df.loc[test_idx, '1step_test24e'] = prev[test_idx] + Ptest24e
df.loc[train_idx, '1step_train25e'] = prev[train_idx] + Ptrain25e
df.loc[test_idx, '1step_test25e'] = prev[test_idx] + Ptest25e
df.loc[train_idx, '1step_train26e'] = prev[train_idx] + Ptrain26e
df.loc[test_idx, '1step_test26e'] = prev[test_idx] + Ptest26e
df.loc[train_idx, '1step_train27e'] = prev[train_idx] + Ptrain27e
df.loc[test_idx, '1step_test27e'] = prev[test_idx] + Ptest27e
df.loc[train_idx, '1step_train28e'] = prev[train_idx] + Ptrain28e
df.loc[test_idx, '1step_test28e'] = prev[test_idx] + Ptest28e
df.loc[train_idx, '1step_train29e'] = prev[train_idx] + Ptrain29e
df.loc[test_idx, '1step_test29e'] = prev[test_idx] + Ptest29e
df.loc[train_idx, '1step_train30e'] = prev[train_idx] + Ptrain30e
df.loc[test_idx, '1step_test30e'] = prev[test_idx] + Ptest30e
df.loc[train_idx, '1step_train31e'] = prev[train_idx] + Ptrain31e
df.loc[test_idx, '1step_test31e'] = prev[test_idx] + Ptest31e
df.loc[train_idx, '1step_train32e'] = prev[train_idx] + Ptrain32e
df.loc[test_idx, '1step_test32e'] = prev[test_idx] + Ptest32e
df.loc[train_idx, '1step_train33e'] = prev[train_idx] + Ptrain33e
df.loc[test_idx, '1step_test33e'] = prev[test_idx] + Ptest33e
df.loc[train_idx, '1step_train34e'] = prev[train_idx] + Ptrain34e
df.loc[test_idx, '1step_test34e'] = prev[test_idx] + Ptest34e
df.loc[train_idx, '1step_train35e'] = prev[train_idx] + Ptrain35e
df.loc[test_idx, '1step_test35e'] = prev[test_idx] + Ptest35e
df.loc[train_idx, '1step_train36e'] = prev[train_idx] + Ptrain36e
df.loc[test_idx, '1step_test36e'] = prev[test_idx] + Ptest36e
# 1-step forecast Bi-LSTM 2 Capa
df.loc[train_idx, '1step_train2f'] = prev[train_idx] + Ptrain3f
df.loc[test_idx, '1step_test2f'] = prev[test_idx] + Ptest3f
df.loc[train_idx, '1step_train3f'] = prev[train_idx] + Ptrain3f
df.loc[test_idx, '1step_test3f'] = prev[test_idx] + Ptest3f
df.loc[train_idx, '1step_train4f'] = prev[train_idx] + Ptrain4f
df.loc[test_idx, '1step_test4f'] = prev[test_idx] + Ptest4f
df.loc[train_idx, '1step_train5f'] = prev[train_idx] + Ptrain5f
df.loc[test_idx, '1step_test5f'] = prev[test_idx] + Ptest5f
df.loc[train_idx, '1step_train6f'] = prev[train_idx] + Ptrain6f
df.loc[test_idx, '1step_test6f'] = prev[test_idx] + Ptest6f
df.loc[train_idx, '1step_train7f'] = prev[train_idx] + Ptrain7f
df.loc[test_idx, '1step_test7f'] = prev[test_idx] + Ptest7f
df.loc[train_idx, '1step_train8f'] = prev[train_idx] + Ptrain8f
df.loc[test_idx, '1step_test8f'] = prev[test_idx] + Ptest8f
df.loc[train_idx, '1step_train9f'] = prev[train_idx] + Ptrain9f
df.loc[test_idx, '1step_test9f'] = prev[test_idx] + Ptest9f
df.loc[train_idx, '1step_train10f'] = prev[train_idx] + Ptrain10f
df.loc[test_idx, '1step_test10f'] = prev[test_idx] + Ptest10f
df.loc[train_idx, '1step_train11f'] = prev[train_idx] + Ptrain11f
df.loc[test_idx, '1step_test11f'] = prev[test_idx] + Ptest11f
df.loc[train_idx, '1step_train12f'] = prev[train_idx] + Ptrain12f
df.loc[test_idx, '1step_test12f'] = prev[test_idx] + Ptest12f
df.loc[train_idx, '1step_train13f'] = prev[train_idx] + Ptrain13f
df.loc[test_idx, '1step_test13f'] = prev[test_idx] + Ptest13f
df.loc[train_idx, '1step_train14f'] = prev[train_idx] + Ptrain14f
df.loc[test_idx, '1step_test14f'] = prev[test_idx] + Ptest14f
df.loc[train_idx, '1step_train15f'] = prev[train_idx] + Ptrain15f
df.loc[test_idx, '1step_test15f'] = prev[test_idx] + Ptest15f
df.loc[train_idx, '1step_train16f'] = prev[train_idx] + Ptrain16f
df.loc[test_idx, '1step_test16f'] = prev[test_idx] + Ptest16f
df.loc[train_idx, '1step_train17f'] = prev[train_idx] + Ptrain17f
df.loc[test_idx, '1step_test17f'] = prev[test_idx] + Ptest17f
df.loc[train_idx, '1step_train18f'] = prev[train_idx] + Ptrain18f
df.loc[test_idx, '1step_test18f'] = prev[test_idx] + Ptest18f
df.loc[train_idx, '1step_train19f'] = prev[train_idx] + Ptrain19f
df.loc[test_idx, '1step_test19f'] = prev[test_idx] + Ptest19f
df.loc[train_idx, '1step_train20f'] = prev[train_idx] + Ptrain20f
df.loc[test_idx, '1step_test20f'] = prev[test_idx] + Ptest20f
df.loc[train_idx, '1step_train21f'] = prev[train_idx] + Ptrain21f
df.loc[test_idx, '1step_test21f'] = prev[test_idx] + Ptest21f
df.loc[train_idx, '1step_train22f'] = prev[train_idx] + Ptrain22f
df.loc[test_idx, '1step_test22f'] = prev[test_idx] + Ptest22f
df.loc[train_idx, '1step_train23f'] = prev[train_idx] + Ptrain23f
df.loc[test_idx, '1step_test23f'] = prev[test_idx] + Ptest23f
df.loc[train_idx, '1step_train24f'] = prev[train_idx] + Ptrain24f
df.loc[test_idx, '1step_test24f'] = prev[test_idx] + Ptest24f
df.loc[train_idx, '1step_train25f'] = prev[train_idx] + Ptrain25f
df.loc[test_idx, '1step_test25f'] = prev[test_idx] + Ptest25f
df.loc[train_idx, '1step_train26f'] = prev[train_idx] + Ptrain26f
df.loc[test_idx, '1step_test26f'] = prev[test_idx] + Ptest26f
df.loc[train_idx, '1step_train27f'] = prev[train_idx] + Ptrain27f
df.loc[test_idx, '1step_test27f'] = prev[test_idx] + Ptest27f
df.loc[train_idx, '1step_train28f'] = prev[train_idx] + Ptrain28f
df.loc[test_idx, '1step_test28f'] = prev[test_idx] + Ptest28f
df.loc[train_idx, '1step_train29f'] = prev[train_idx] + Ptrain29f
df.loc[test_idx, '1step_test29f'] = prev[test_idx] + Ptest29f
df.loc[train_idx, '1step_train30f'] = prev[train_idx] + Ptrain30f
df.loc[test_idx, '1step_test30f'] = prev[test_idx] + Ptest30f
df.loc[train_idx, '1step_train31f'] = prev[train_idx] + Ptrain31f
df.loc[test_idx, '1step_test31f'] = prev[test_idx] + Ptest31f
df.loc[train_idx, '1step_train32f'] = prev[train_idx] + Ptrain32f
df.loc[test_idx, '1step_test32f'] = prev[test_idx] + Ptest32f
df.loc[train_idx, '1step_train33f'] = prev[train_idx] + Ptrain33f
df.loc[test_idx, '1step_test33f'] = prev[test_idx] + Ptest33f
df.loc[train_idx, '1step_train34f'] = prev[train_idx] + Ptrain34f
df.loc[test_idx, '1step_test34f'] = prev[test_idx] + Ptest34f
df.loc[train_idx, '1step_train35f'] = prev[train_idx] + Ptrain35f
df.loc[test_idx, '1step_test35f'] = prev[test_idx] + Ptest35f
df.loc[train_idx, '1step_train36f'] = prev[train_idx] + Ptrain36f
df.loc[test_idx, '1step_test36f'] = prev[test_idx] + Ptest36f
#Vuelve a recorrerse a donde estaba situado. RNN 1 Capa
df.loc[test_idx, '1step_test2'] = df.loc[test_idx, '1step_test2'].shift(-1)
df.loc[train_idx, '1step_train2'] = df.loc[train_idx, '1step_train2'].shift(-1)
df.loc[test_idx, '1step_test3'] = df.loc[test_idx, '1step_test3'].shift(-1)
df.loc[train_idx, '1step_train3'] = df.loc[train_idx, '1step_train3'].shift(-1)
df.loc[test_idx, '1step_test4'] = df.loc[test_idx, '1step_test4'].shift(-1)
df.loc[train_idx, '1step_train4'] = df.loc[train_idx, '1step_train4'].shift(-1)
df.loc[test_idx, '1step_test5'] = df.loc[test_idx, '1step_test5'].shift(-1)
df.loc[train_idx, '1step_train5'] = df.loc[train_idx, '1step_train5'].shift(-1)
df.loc[test_idx, '1step_test6'] = df.loc[test_idx, '1step_test6'].shift(-1)
df.loc[train_idx, '1step_train6'] = df.loc[train_idx, '1step_train6'].shift(-1)
df.loc[test_idx, '1step_test7'] = df.loc[test_idx, '1step_test7'].shift(-1)
df.loc[train_idx, '1step_train7'] = df.loc[train_idx, '1step_train7'].shift(-1)
df.loc[test_idx, '1step_test8'] = df.loc[test_idx, '1step_test8'].shift(-1)
df.loc[train_idx, '1step_train8'] = df.loc[train_idx, '1step_train8'].shift(-1)
df.loc[test_idx, '1step_test9'] = df.loc[test_idx, '1step_test9'].shift(-1)
df.loc[train_idx, '1step_train9'] = df.loc[train_idx, '1step_train9'].shift(-1)
df.loc[test_idx, '1step_test10'] = df.loc[test_idx, '1step_test10'].shift(-1)
df.loc[train_idx, '1step_train10'] = df.loc[train_idx, '1step_train10'].shift(-1)
df.loc[test_idx, '1step_test11'] = df.loc[test_idx, '1step_test11'].shift(-1)
df.loc[train_idx, '1step_train11'] = df.loc[train_idx, '1step_train11'].shift(-1)
df.loc[test_idx, '1step_test12'] = df.loc[test_idx, '1step_test12'].shift(-1)
df.loc[train_idx, '1step_train12'] = df.loc[train_idx, '1step_train12'].shift(-1)
df.loc[test_idx, '1step_test13'] = df.loc[test_idx, '1step_test13'].shift(-1)
df.loc[train_idx, '1step_train13'] = df.loc[train_idx, '1step_train13'].shift(-1)
df.loc[test_idx, '1step_test14'] = df.loc[test_idx, '1step_test14'].shift(-1)
df.loc[train_idx, '1step_train14'] = df.loc[train_idx, '1step_train14'].shift(-1)
df.loc[test_idx, '1step_test15'] = df.loc[test_idx, '1step_test15'].shift(-1)
df.loc[train_idx, '1step_train15'] = df.loc[train_idx, '1step_train15'].shift(-1)
df.loc[test_idx, '1step_test16'] = df.loc[test_idx, '1step_test16'].shift(-1)
df.loc[train_idx, '1step_train16'] = df.loc[train_idx, '1step_train16'].shift(-1)
df.loc[test_idx, '1step_test17'] = df.loc[test_idx, '1step_test17'].shift(-1)
df.loc[train_idx, '1step_train17'] = df.loc[train_idx, '1step_train17'].shift(-1)
df.loc[test_idx, '1step_test18'] = df.loc[test_idx, '1step_test18'].shift(-1)
df.loc[train_idx, '1step_train18'] = df.loc[train_idx, '1step_train18'].shift(-1)
df.loc[test_idx, '1step_test19'] = df.loc[test_idx, '1step_test19'].shift(-1)
df.loc[train_idx, '1step_train19'] = df.loc[train_idx, '1step_train19'].shift(-1)
df.loc[test_idx, '1step_test20'] = df.loc[test_idx, '1step_test20'].shift(-1)
df.loc[train_idx, '1step_train20'] = df.loc[train_idx, '1step_train20'].shift(-1)
df.loc[test_idx, '1step_test21'] = df.loc[test_idx, '1step_test21'].shift(-1)
df.loc[train_idx, '1step_train21'] = df.loc[train_idx, '1step_train21'].shift(-1)
df.loc[test_idx, '1step_test22'] = df.loc[test_idx, '1step_test22'].shift(-1)
df.loc[train_idx, '1step_train22'] = df.loc[train_idx, '1step_train22'].shift(-1)
df.loc[test_idx, '1step_test23'] = df.loc[test_idx, '1step_test23'].shift(-1)
df.loc[train_idx, '1step_train23'] = df.loc[train_idx, '1step_train23'].shift(-1)
df.loc[test_idx, '1step_test24'] = df.loc[test_idx, '1step_test24'].shift(-1)
df.loc[train_idx, '1step_train24'] = df.loc[train_idx, '1step_train24'].shift(-1)
df.loc[test_idx, '1step_test25'] = df.loc[test_idx, '1step_test25'].shift(-1)
df.loc[train_idx, '1step_train25'] = df.loc[train_idx, '1step_train25'].shift(-1)
df.loc[test_idx, '1step_test26'] = df.loc[test_idx, '1step_test26'].shift(-1)
df.loc[train_idx, '1step_train26'] = df.loc[train_idx, '1step_train26'].shift(-1)
df.loc[test_idx, '1step_test27'] = df.loc[test_idx, '1step_test27'].shift(-1)
df.loc[train_idx, '1step_train27'] = df.loc[train_idx, '1step_train27'].shift(-1)
df.loc[test_idx, '1step_test28'] = df.loc[test_idx, '1step_test28'].shift(-1)
df.loc[train_idx, '1step_train28'] = df.loc[train_idx, '1step_train28'].shift(-1)
df.loc[test_idx, '1step_test29'] = df.loc[test_idx, '1step_test29'].shift(-1)
df.loc[train_idx, '1step_train29'] = df.loc[train_idx, '1step_train29'].shift(-1)
df.loc[test_idx, '1step_test30'] = df.loc[test_idx, '1step_test30'].shift(-1)
df.loc[train_idx, '1step_train30'] = df.loc[train_idx, '1step_train30'].shift(-1)
df.loc[test_idx, '1step_test31'] = df.loc[test_idx, '1step_test31'].shift(-1)
df.loc[train_idx, '1step_train31'] = df.loc[train_idx, '1step_train31'].shift(-1)
df.loc[test_idx, '1step_test32'] = df.loc[test_idx, '1step_test32'].shift(-1)
df.loc[train_idx, '1step_train32'] = df.loc[train_idx, '1step_train32'].shift(-1)
df.loc[test_idx, '1step_test33'] = df.loc[test_idx, '1step_test33'].shift(-1)
df.loc[train_idx, '1step_train33'] = df.loc[train_idx, '1step_train33'].shift(-1)
df.loc[test_idx, '1step_test34'] = df.loc[test_idx, '1step_test34'].shift(-1)
df.loc[train_idx, '1step_train34'] = df.loc[train_idx, '1step_train34'].shift(-1)
df.loc[test_idx, '1step_test35'] = df.loc[test_idx, '1step_test35'].shift(-1)
df.loc[train_idx, '1step_train35'] = df.loc[train_idx, '1step_train35'].shift(-1)
df.loc[test_idx, '1step_test36'] = df.loc[test_idx, '1step_test36'].shift(-1)
df.loc[train_idx, '1step_train36'] = df.loc[train_idx, '1step_train36'].shift(-1)
#Regreso los datos diferenciados a su fecha orginal RNN 2 Capas
df.loc[test_idx, '1step_test2b'] = df.loc[test_idx, '1step_test2b'].shift(-1)
df.loc[train_idx, '1step_train2b'] = df.loc[train_idx, '1step_train2b'].shift(-1)
df.loc[test_idx, '1step_test3b'] = df.loc[test_idx, '1step_test3b'].shift(-1)
df.loc[train_idx, '1step_train3b'] = df.loc[train_idx, '1step_train3b'].shift(-1)
df.loc[test_idx, '1step_test4b'] = df.loc[test_idx, '1step_test4b'].shift(-1)
df.loc[train_idx, '1step_train4b'] = df.loc[train_idx, '1step_train4b'].shift(-1)
df.loc[test_idx, '1step_test5b'] = df.loc[test_idx, '1step_test5b'].shift(-1)
df.loc[train_idx, '1step_train5b'] = df.loc[train_idx, '1step_train5b'].shift(-1)
df.loc[test_idx, '1step_test6b'] = df.loc[test_idx, '1step_test6b'].shift(-1)
df.loc[train_idx, '1step_train6b'] = df.loc[train_idx, '1step_train6b'].shift(-1)
df.loc[test_idx, '1step_test7b'] = df.loc[test_idx, '1step_test7b'].shift(-1)
df.loc[train_idx, '1step_train7b'] = df.loc[train_idx, '1step_train7b'].shift(-1)
df.loc[test_idx, '1step_test8b'] = df.loc[test_idx, '1step_test8b'].shift(-1)
df.loc[train_idx, '1step_train8b'] = df.loc[train_idx, '1step_train8b'].shift(-1)
df.loc[test_idx, '1step_test9b'] = df.loc[test_idx, '1step_test9b'].shift(-1)
df.loc[train_idx, '1step_train9b'] = df.loc[train_idx, '1step_train9b'].shift(-1)
df.loc[test_idx, '1step_test10b'] = df.loc[test_idx, '1step_test10b'].shift(-1)
df.loc[train_idx, '1step_train10b'] = df.loc[train_idx, '1step_train10b'].shift(-1)
df.loc[test_idx, '1step_test11b'] = df.loc[test_idx, '1step_test11b'].shift(-1)
df.loc[train_idx, '1step_train11b'] = df.loc[train_idx, '1step_train11b'].shift(-1)
df.loc[test_idx, '1step_test12b'] = df.loc[test_idx, '1step_test12b'].shift(-1)
df.loc[train_idx, '1step_train12b'] = df.loc[train_idx, '1step_train12b'].shift(-1)
df.loc[test_idx, '1step_test13b'] = df.loc[test_idx, '1step_test13b'].shift(-1)
df.loc[train_idx, '1step_train13b'] = df.loc[train_idx, '1step_train13b'].shift(-1)
df.loc[test_idx, '1step_test14b'] = df.loc[test_idx, '1step_test14b'].shift(-1)
df.loc[train_idx, '1step_train14b'] = df.loc[train_idx, '1step_train14b'].shift(-1)
df.loc[test_idx, '1step_test15b'] = df.loc[test_idx, '1step_test15b'].shift(-1)
df.loc[train_idx, '1step_train15b'] = df.loc[train_idx, '1step_train15b'].shift(-1)
df.loc[test_idx, '1step_test16b'] = df.loc[test_idx, '1step_test16b'].shift(-1)
df.loc[train_idx, '1step_train16b'] = df.loc[train_idx, '1step_train16b'].shift(-1)
df.loc[test_idx, '1step_test17b'] = df.loc[test_idx, '1step_test17b'].shift(-1)
df.loc[train_idx, '1step_train17b'] = df.loc[train_idx, '1step_train17b'].shift(-1)
df.loc[test_idx, '1step_test18b'] = df.loc[test_idx, '1step_test18b'].shift(-1)
df.loc[train_idx, '1step_train18b'] = df.loc[train_idx, '1step_train18b'].shift(-1)
df.loc[test_idx, '1step_test19b'] = df.loc[test_idx, '1step_test19b'].shift(-1)
df.loc[train_idx, '1step_train19b'] = df.loc[train_idx, '1step_train19b'].shift(-1)
df.loc[test_idx, '1step_test20b'] = df.loc[test_idx, '1step_test20b'].shift(-1)
df.loc[train_idx, '1step_train20b'] = df.loc[train_idx, '1step_train20b'].shift(-1)
df.loc[test_idx, '1step_test21b'] = df.loc[test_idx, '1step_test21b'].shift(-1)
df.loc[train_idx, '1step_train21b'] = df.loc[train_idx, '1step_train21b'].shift(-1)
df.loc[test_idx, '1step_test22b'] = df.loc[test_idx, '1step_test22b'].shift(-1)
df.loc[train_idx, '1step_train22b'] = df.loc[train_idx, '1step_train22b'].shift(-1)
df.loc[test_idx, '1step_test23b'] = df.loc[test_idx, '1step_test23b'].shift(-1)
df.loc[train_idx, '1step_train23b'] = df.loc[train_idx, '1step_train23b'].shift(-1)
df.loc[test_idx, '1step_test24b'] = df.loc[test_idx, '1step_test24b'].shift(-1)
df.loc[train_idx, '1step_train24b'] = df.loc[train_idx, '1step_train24b'].shift(-1)
df.loc[test_idx, '1step_test25b'] = df.loc[test_idx, '1step_test25b'].shift(-1)
df.loc[train_idx, '1step_train25b'] = df.loc[train_idx, '1step_train25b'].shift(-1)
df.loc[test_idx, '1step_test26b'] = df.loc[test_idx, '1step_test26b'].shift(-1)
df.loc[train_idx, '1step_train26b'] = df.loc[train_idx, '1step_train26b'].shift(-1)
df.loc[test_idx, '1step_test27b'] = df.loc[test_idx, '1step_test27b'].shift(-1)
df.loc[train_idx, '1step_train27b'] = df.loc[train_idx, '1step_train27b'].shift(-1)
df.loc[test_idx, '1step_test28b'] = df.loc[test_idx, '1step_test28b'].shift(-1)
df.loc[train_idx, '1step_train28b'] = df.loc[train_idx, '1step_train28b'].shift(-1)
df.loc[test_idx, '1step_test29b'] = df.loc[test_idx, '1step_test29b'].shift(-1)
df.loc[train_idx, '1step_train29b'] = df.loc[train_idx, '1step_train29b'].shift(-1)
df.loc[test_idx, '1step_test30b'] = df.loc[test_idx, '1step_test30b'].shift(-1)
df.loc[train_idx, '1step_train30b'] = df.loc[train_idx, '1step_train30b'].shift(-1)
df.loc[test_idx, '1step_test31b'] = df.loc[test_idx, '1step_test31b'].shift(-1)
df.loc[train_idx, '1step_train31b'] = df.loc[train_idx, '1step_train31b'].shift(-1)
df.loc[test_idx, '1step_test32b'] = df.loc[test_idx, '1step_test32b'].shift(-1)
df.loc[train_idx, '1step_train32b'] = df.loc[train_idx, '1step_train32b'].shift(-1)
df.loc[test_idx, '1step_test33b'] = df.loc[test_idx, '1step_test33b'].shift(-1)
df.loc[train_idx, '1step_train33b'] = df.loc[train_idx, '1step_train33b'].shift(-1)
df.loc[test_idx, '1step_test34b'] = df.loc[test_idx, '1step_test34b'].shift(-1)
df.loc[train_idx, '1step_train34b'] = df.loc[train_idx, '1step_train34b'].shift(-1)
df.loc[test_idx, '1step_test35b'] = df.loc[test_idx, '1step_test35b'].shift(-1)
df.loc[train_idx, '1step_train35b'] = df.loc[train_idx, '1step_train35b'].shift(-1)
df.loc[test_idx, '1step_test36b'] = df.loc[test_idx, '1step_test36b'].shift(-1)
df.loc[train_idx, '1step_train36b'] = df.loc[train_idx, '1step_train36b'].shift(-1)
#Regreso los datos diferenciados a su fecha original LSTM 1 Capa
df.loc[test_idx, '1step_test2c'] = df.loc[test_idx, '1step_test2c'].shift(-1)
df.loc[train_idx, '1step_train2c'] = df.loc[train_idx, '1step_train2c'].shift(-1)
df.loc[test_idx, '1step_test3c'] = df.loc[test_idx, '1step_test3c'].shift(-1)
df.loc[train_idx, '1step_train3c'] = df.loc[train_idx, '1step_train3c'].shift(-1)
df.loc[test_idx, '1step_test4c'] = df.loc[test_idx, '1step_test4c'].shift(-1)
df.loc[train_idx, '1step_train4c'] = df.loc[train_idx, '1step_train4c'].shift(-1)
df.loc[test_idx, '1step_test5c'] = df.loc[test_idx, '1step_test5c'].shift(-1)
df.loc[train_idx, '1step_train5c'] = df.loc[train_idx, '1step_train5c'].shift(-1)
df.loc[test_idx, '1step_test6c'] = df.loc[test_idx, '1step_test6c'].shift(-1)
df.loc[train_idx, '1step_train6c'] = df.loc[train_idx, '1step_train6c'].shift(-1)
df.loc[test_idx, '1step_test7c'] = df.loc[test_idx, '1step_test7c'].shift(-1)
df.loc[train_idx, '1step_train7c'] = df.loc[train_idx, '1step_train7c'].shift(-1)
df.loc[test_idx, '1step_test8c'] = df.loc[test_idx, '1step_test8c'].shift(-1)
df.loc[train_idx, '1step_train8c'] = df.loc[train_idx, '1step_train8c'].shift(-1)
df.loc[test_idx, '1step_test9c'] = df.loc[test_idx, '1step_test9c'].shift(-1)
df.loc[train_idx, '1step_train9c'] = df.loc[train_idx, '1step_train9c'].shift(-1)
df.loc[test_idx, '1step_test10c'] = df.loc[test_idx, '1step_test10c'].shift(-1)
df.loc[train_idx, '1step_train10c'] = df.loc[train_idx, '1step_train10c'].shift(-1)
df.loc[test_idx, '1step_test11c'] = df.loc[test_idx, '1step_test11c'].shift(-1)
df.loc[train_idx, '1step_train11c'] = df.loc[train_idx, '1step_train11c'].shift(-1)
df.loc[test_idx, '1step_test12c'] = df.loc[test_idx, '1step_test12c'].shift(-1)
df.loc[train_idx, '1step_train12c'] = df.loc[train_idx, '1step_train12c'].shift(-1)
df.loc[test_idx, '1step_test13c'] = df.loc[test_idx, '1step_test13c'].shift(-1)
df.loc[train_idx, '1step_train13c'] = df.loc[train_idx, '1step_train13c'].shift(-1)
df.loc[test_idx, '1step_test14c'] = df.loc[test_idx, '1step_test14c'].shift(-1)
df.loc[train_idx, '1step_train14c'] = df.loc[train_idx, '1step_train14c'].shift(-1)
df.loc[test_idx, '1step_test15c'] = df.loc[test_idx, '1step_test15c'].shift(-1)
df.loc[train_idx, '1step_train15c'] = df.loc[train_idx, '1step_train15c'].shift(-1)
df.loc[test_idx, '1step_test16c'] = df.loc[test_idx, '1step_test16c'].shift(-1)
df.loc[train_idx, '1step_train16c'] = df.loc[train_idx, '1step_train16c'].shift(-1)
df.loc[test_idx, '1step_test17c'] = df.loc[test_idx, '1step_test17c'].shift(-1)
df.loc[train_idx, '1step_train17c'] = df.loc[train_idx, '1step_train17c'].shift(-1)
df.loc[test_idx, '1step_test18c'] = df.loc[test_idx, '1step_test18c'].shift(-1)
df.loc[train_idx, '1step_train18c'] = df.loc[train_idx, '1step_train18c'].shift(-1)
df.loc[test_idx, '1step_test19c'] = df.loc[test_idx, '1step_test19c'].shift(-1)
df.loc[train_idx, '1step_train19c'] = df.loc[train_idx, '1step_train19c'].shift(-1)
df.loc[test_idx, '1step_test20c'] = df.loc[test_idx, '1step_test20c'].shift(-1)
df.loc[train_idx, '1step_train20c'] = df.loc[train_idx, '1step_train20c'].shift(-1)
df.loc[test_idx, '1step_test21c'] = df.loc[test_idx, '1step_test21c'].shift(-1)
df.loc[train_idx, '1step_train21c'] = df.loc[train_idx, '1step_train21c'].shift(-1)
df.loc[test_idx, '1step_test22c'] = df.loc[test_idx, '1step_test22c'].shift(-1)
df.loc[train_idx, '1step_train22c'] = df.loc[train_idx, '1step_train22c'].shift(-1)
df.loc[test_idx, '1step_test23c'] = df.loc[test_idx, '1step_test23c'].shift(-1)
df.loc[train_idx, '1step_train23c'] = df.loc[train_idx, '1step_train23c'].shift(-1)
df.loc[test_idx, '1step_test24c'] = df.loc[test_idx, '1step_test24c'].shift(-1)
df.loc[train_idx, '1step_train24c'] = df.loc[train_idx, '1step_train24c'].shift(-1)
df.loc[test_idx, '1step_test25c'] = df.loc[test_idx, '1step_test25c'].shift(-1)
df.loc[train_idx, '1step_train25c'] = df.loc[train_idx, '1step_train25c'].shift(-1)
df.loc[test_idx, '1step_test26c'] = df.loc[test_idx, '1step_test26c'].shift(-1)
df.loc[train_idx, '1step_train26c'] = df.loc[train_idx, '1step_train26c'].shift(-1)
df.loc[test_idx, '1step_test27c'] = df.loc[test_idx, '1step_test27c'].shift(-1)
df.loc[train_idx, '1step_train27c'] = df.loc[train_idx, '1step_train27c'].shift(-1)
df.loc[test_idx, '1step_test28c'] = df.loc[test_idx, '1step_test28c'].shift(-1)
df.loc[train_idx, '1step_train28c'] = df.loc[train_idx, '1step_train28c'].shift(-1)
df.loc[test_idx, '1step_test29c'] = df.loc[test_idx, '1step_test29c'].shift(-1)
df.loc[train_idx, '1step_train29c'] = df.loc[train_idx, '1step_train29c'].shift(-1)
df.loc[test_idx, '1step_test30c'] = df.loc[test_idx, '1step_test30c'].shift(-1)
df.loc[train_idx, '1step_train30c'] = df.loc[train_idx, '1step_train30c'].shift(-1)
df.loc[test_idx, '1step_test31c'] = df.loc[test_idx, '1step_test31c'].shift(-1)
df.loc[train_idx, '1step_train31c'] = df.loc[train_idx, '1step_train31c'].shift(-1)
df.loc[test_idx, '1step_test32c'] = df.loc[test_idx, '1step_test32c'].shift(-1)
df.loc[train_idx, '1step_train32c'] = df.loc[train_idx, '1step_train32c'].shift(-1)
df.loc[test_idx, '1step_test33c'] = df.loc[test_idx, '1step_test33c'].shift(-1)
df.loc[train_idx, '1step_train33c'] = df.loc[train_idx, '1step_train33c'].shift(-1)
df.loc[test_idx, '1step_test34c'] = df.loc[test_idx, '1step_test34c'].shift(-1)
df.loc[train_idx, '1step_train34c'] = df.loc[train_idx, '1step_train34c'].shift(-1)
df.loc[test_idx, '1step_test35c'] = df.loc[test_idx, '1step_test35c'].shift(-1)
df.loc[train_idx, '1step_train35c'] = df.loc[train_idx, '1step_train35c'].shift(-1)
df.loc[test_idx, '1step_test36c'] = df.loc[test_idx, '1step_test36c'].shift(-1)
df.loc[train_idx, '1step_train36c'] = df.loc[train_idx, '1step_train36c'].shift(-1)
#Regreso los datos diferenciados a su fecha original LSTM 2 Capas
df.loc[test_idx, '1step_test2d'] = df.loc[test_idx, '1step_test2d'].shift(-1)
df.loc[train_idx, '1step_train2d'] = df.loc[train_idx, '1step_train2d'].shift(-1)
df.loc[test_idx, '1step_test3d'] = df.loc[test_idx, '1step_test3d'].shift(-1)
df.loc[train_idx, '1step_train3d'] = df.loc[train_idx, '1step_train3d'].shift(-1)
df.loc[test_idx, '1step_test4d'] = df.loc[test_idx, '1step_test4d'].shift(-1)
df.loc[train_idx, '1step_train4d'] = df.loc[train_idx, '1step_train4d'].shift(-1)
df.loc[test_idx, '1step_test5d'] = df.loc[test_idx, '1step_test5d'].shift(-1)
df.loc[train_idx, '1step_train5d'] = df.loc[train_idx, '1step_train5d'].shift(-1)
df.loc[test_idx, '1step_test6d'] = df.loc[test_idx, '1step_test6d'].shift(-1)
df.loc[train_idx, '1step_train6d'] = df.loc[train_idx, '1step_train6d'].shift(-1)
df.loc[test_idx, '1step_test7d'] = df.loc[test_idx, '1step_test7d'].shift(-1)
df.loc[train_idx, '1step_train7d'] = df.loc[train_idx, '1step_train7d'].shift(-1)
df.loc[test_idx, '1step_test8d'] = df.loc[test_idx, '1step_test8d'].shift(-1)
df.loc[train_idx, '1step_train8d'] = df.loc[train_idx, '1step_train8d'].shift(-1)
df.loc[test_idx, '1step_test9d'] = df.loc[test_idx, '1step_test9d'].shift(-1)
df.loc[train_idx, '1step_train9d'] = df.loc[train_idx, '1step_train9d'].shift(-1)
df.loc[test_idx, '1step_test10d'] = df.loc[test_idx, '1step_test10d'].shift(-1)
df.loc[train_idx, '1step_train10d'] = df.loc[train_idx, '1step_train10d'].shift(-1)
df.loc[test_idx, '1step_test11d'] = df.loc[test_idx, '1step_test11d'].shift(-1)
df.loc[train_idx, '1step_train11d'] = df.loc[train_idx, '1step_train11d'].shift(-1)
df.loc[test_idx, '1step_test12d'] = df.loc[test_idx, '1step_test12d'].shift(-1)
df.loc[train_idx, '1step_train12d'] = df.loc[train_idx, '1step_train12d'].shift(-1)
df.loc[test_idx, '1step_test13d'] = df.loc[test_idx, '1step_test13d'].shift(-1)
df.loc[train_idx, '1step_train13d'] = df.loc[train_idx, '1step_train13d'].shift(-1)
df.loc[test_idx, '1step_test14d'] = df.loc[test_idx, '1step_test14d'].shift(-1)
df.loc[train_idx, '1step_train14d'] = df.loc[train_idx, '1step_train14d'].shift(-1)
df.loc[test_idx, '1step_test15d'] = df.loc[test_idx, '1step_test15d'].shift(-1)
df.loc[train_idx, '1step_train15d'] = df.loc[train_idx, '1step_train15d'].shift(-1)
df.loc[test_idx, '1step_test16d'] = df.loc[test_idx, '1step_test16d'].shift(-1)
df.loc[train_idx, '1step_train16d'] = df.loc[train_idx, '1step_train16d'].shift(-1)
df.loc[test_idx, '1step_test17d'] = df.loc[test_idx, '1step_test17d'].shift(-1)
df.loc[train_idx, '1step_train17d'] = df.loc[train_idx, '1step_train17d'].shift(-1)
df.loc[test_idx, '1step_test18d'] = df.loc[test_idx, '1step_test18d'].shift(-1)
df.loc[train_idx, '1step_train18d'] = df.loc[train_idx, '1step_train18d'].shift(-1)
df.loc[test_idx, '1step_test19d'] = df.loc[test_idx, '1step_test19d'].shift(-1)
df.loc[train_idx, '1step_train19d'] = df.loc[train_idx, '1step_train19d'].shift(-1)
df.loc[test_idx, '1step_test20d'] = df.loc[test_idx, '1step_test20d'].shift(-1)
df.loc[train_idx, '1step_train20d'] = df.loc[train_idx, '1step_train20d'].shift(-1)
df.loc[test_idx, '1step_test21d'] = df.loc[test_idx, '1step_test21d'].shift(-1)
df.loc[train_idx, '1step_train21d'] = df.loc[train_idx, '1step_train21d'].shift(-1)
df.loc[test_idx, '1step_test22d'] = df.loc[test_idx, '1step_test22d'].shift(-1)
df.loc[train_idx, '1step_train22d'] = df.loc[train_idx, '1step_train22d'].shift(-1)
df.loc[test_idx, '1step_test23d'] = df.loc[test_idx, '1step_test23d'].shift(-1)
df.loc[train_idx, '1step_train23d'] = df.loc[train_idx, '1step_train23d'].shift(-1)
df.loc[test_idx, '1step_test24d'] = df.loc[test_idx, '1step_test24d'].shift(-1)
df.loc[train_idx, '1step_train24d'] = df.loc[train_idx, '1step_train24d'].shift(-1)
df.loc[test_idx, '1step_test25d'] = df.loc[test_idx, '1step_test25d'].shift(-1)
df.loc[train_idx, '1step_train25d'] = df.loc[train_idx, '1step_train25d'].shift(-1)
df.loc[test_idx, '1step_test26d'] = df.loc[test_idx, '1step_test26d'].shift(-1)
df.loc[train_idx, '1step_train26d'] = df.loc[train_idx, '1step_train26d'].shift(-1)
df.loc[test_idx, '1step_test27d'] = df.loc[test_idx, '1step_test27d'].shift(-1)
df.loc[train_idx, '1step_train27d'] = df.loc[train_idx, '1step_train27d'].shift(-1)
df.loc[test_idx, '1step_test28d'] = df.loc[test_idx, '1step_test28d'].shift(-1)
df.loc[train_idx, '1step_train28d'] = df.loc[train_idx, '1step_train28d'].shift(-1)
df.loc[test_idx, '1step_test29d'] = df.loc[test_idx, '1step_test29d'].shift(-1)
df.loc[train_idx, '1step_train29d'] = df.loc[train_idx, '1step_train29d'].shift(-1)
df.loc[test_idx, '1step_test30d'] = df.loc[test_idx, '1step_test30d'].shift(-1)
df.loc[train_idx, '1step_train30d'] = df.loc[train_idx, '1step_train30d'].shift(-1)
df.loc[test_idx, '1step_test31d'] = df.loc[test_idx, '1step_test31d'].shift(-1)
df.loc[train_idx, '1step_train31d'] = df.loc[train_idx, '1step_train31d'].shift(-1)
df.loc[test_idx, '1step_test32d'] = df.loc[test_idx, '1step_test32d'].shift(-1)
df.loc[train_idx, '1step_train32d'] = df.loc[train_idx, '1step_train32d'].shift(-1)
df.loc[test_idx, '1step_test33d'] = df.loc[test_idx, '1step_test33d'].shift(-1)
df.loc[train_idx, '1step_train33d'] = df.loc[train_idx, '1step_train33d'].shift(-1)
df.loc[test_idx, '1step_test34d'] = df.loc[test_idx, '1step_test34d'].shift(-1)
df.loc[train_idx, '1step_train34d'] = df.loc[train_idx, '1step_train34d'].shift(-1)
df.loc[test_idx, '1step_test35d'] = df.loc[test_idx, '1step_test35d'].shift(-1)
df.loc[train_idx, '1step_train35d'] = df.loc[train_idx, '1step_train35d'].shift(-1)
df.loc[test_idx, '1step_test36d'] = df.loc[test_idx, '1step_test36d'].shift(-1)
df.loc[train_idx, '1step_train36d'] = df.loc[train_idx, '1step_train36d'].shift(-1)
#Regreso los datos diferenciados a su fecha original 2 BI-LSTM 2 Capas
df.loc[test_idx, '1step_test2e'] = df.loc[test_idx, '1step_test2e'].shift(-1)
df.loc[train_idx, '1step_train2e'] = df.loc[train_idx, '1step_train2e'].shift(-1)
df.loc[test_idx, '1step_test3e'] = df.loc[test_idx, '1step_test3e'].shift(-1)
df.loc[train_idx, '1step_train3e'] = df.loc[train_idx, '1step_train3e'].shift(-1)
df.loc[test_idx, '1step_test4e'] = df.loc[test_idx, '1step_test4e'].shift(-1)
df.loc[train_idx, '1step_train4e'] = df.loc[train_idx, '1step_train4e'].shift(-1)
df.loc[test_idx, '1step_test5e'] = df.loc[test_idx, '1step_test5e'].shift(-1)
df.loc[train_idx, '1step_train5e'] = df.loc[train_idx, '1step_train5e'].shift(-1)
df.loc[test_idx, '1step_test6e'] = df.loc[test_idx, '1step_test6e'].shift(-1)
df.loc[train_idx, '1step_train6e'] = df.loc[train_idx, '1step_train6e'].shift(-1)
df.loc[test_idx, '1step_test7e'] = df.loc[test_idx, '1step_test7e'].shift(-1)
df.loc[train_idx, '1step_train7e'] = df.loc[train_idx, '1step_train7e'].shift(-1)
df.loc[test_idx, '1step_test8e'] = df.loc[test_idx, '1step_test8e'].shift(-1)
df.loc[train_idx, '1step_train8e'] = df.loc[train_idx, '1step_train8e'].shift(-1)
df.loc[test_idx, '1step_test9e'] = df.loc[test_idx, '1step_test9e'].shift(-1)
df.loc[train_idx, '1step_train9e'] = df.loc[train_idx, '1step_train9e'].shift(-1)
df.loc[test_idx, '1step_test10e'] = df.loc[test_idx, '1step_test10e'].shift(-1)
df.loc[train_idx, '1step_train10e'] = df.loc[train_idx, '1step_train10e'].shift(-1)
df.loc[test_idx, '1step_test11e'] = df.loc[test_idx, '1step_test11e'].shift(-1)
df.loc[train_idx, '1step_train11e'] = df.loc[train_idx, '1step_train11e'].shift(-1)
df.loc[test_idx, '1step_test12e'] = df.loc[test_idx, '1step_test12e'].shift(-1)
df.loc[train_idx, '1step_train12e'] = df.loc[train_idx, '1step_train12e'].shift(-1)
df.loc[test_idx, '1step_test13e'] = df.loc[test_idx, '1step_test13e'].shift(-1)
df.loc[train_idx, '1step_train13e'] = df.loc[train_idx, '1step_train13e'].shift(-1)
df.loc[test_idx, '1step_test14e'] = df.loc[test_idx, '1step_test14e'].shift(-1)
df.loc[train_idx, '1step_train14e'] = df.loc[train_idx, '1step_train14e'].shift(-1)
df.loc[test_idx, '1step_test15e'] = df.loc[test_idx, '1step_test15e'].shift(-1)
df.loc[train_idx, '1step_train15e'] = df.loc[train_idx, '1step_train15e'].shift(-1)
df.loc[test_idx, '1step_test16e'] = df.loc[test_idx, '1step_test16e'].shift(-1)
df.loc[train_idx, '1step_train16e'] = df.loc[train_idx, '1step_train16e'].shift(-1)
df.loc[test_idx, '1step_test17e'] = df.loc[test_idx, '1step_test17e'].shift(-1)
df.loc[train_idx, '1step_train17e'] = df.loc[train_idx, '1step_train17e'].shift(-1)
df.loc[test_idx, '1step_test18e'] = df.loc[test_idx, '1step_test18e'].shift(-1)
df.loc[train_idx, '1step_train18e'] = df.loc[train_idx, '1step_train18e'].shift(-1)
df.loc[test_idx, '1step_test19e'] = df.loc[test_idx, '1step_test19e'].shift(-1)
df.loc[train_idx, '1step_train19e'] = df.loc[train_idx, '1step_train19e'].shift(-1)
df.loc[test_idx, '1step_test20e'] = df.loc[test_idx, '1step_test20e'].shift(-1)
df.loc[train_idx, '1step_train20e'] = df.loc[train_idx, '1step_train20e'].shift(-1)
df.loc[test_idx, '1step_test21e'] = df.loc[test_idx, '1step_test21e'].shift(-1)
df.loc[train_idx, '1step_train21e'] = df.loc[train_idx, '1step_train21e'].shift(-1)
df.loc[test_idx, '1step_test22e'] = df.loc[test_idx, '1step_test22e'].shift(-1)
df.loc[train_idx, '1step_train22e'] = df.loc[train_idx, '1step_train22e'].shift(-1)
df.loc[test_idx, '1step_test23e'] = df.loc[test_idx, '1step_test23e'].shift(-1)
df.loc[train_idx, '1step_train23e'] = df.loc[train_idx, '1step_train23e'].shift(-1)
df.loc[test_idx, '1step_test24e'] = df.loc[test_idx, '1step_test24e'].shift(-1)
df.loc[train_idx, '1step_train24e'] = df.loc[train_idx, '1step_train24e'].shift(-1)
df.loc[test_idx, '1step_test25e'] = df.loc[test_idx, '1step_test25e'].shift(-1)
df.loc[train_idx, '1step_train25e'] = df.loc[train_idx, '1step_train25e'].shift(-1)
df.loc[test_idx, '1step_test26e'] = df.loc[test_idx, '1step_test26e'].shift(-1)
df.loc[train_idx, '1step_train26e'] = df.loc[train_idx, '1step_train26e'].shift(-1)
df.loc[test_idx, '1step_test27e'] = df.loc[test_idx, '1step_test27e'].shift(-1)
df.loc[train_idx, '1step_train27e'] = df.loc[train_idx, '1step_train27e'].shift(-1)
df.loc[test_idx, '1step_test28e'] = df.loc[test_idx, '1step_test28e'].shift(-1)
df.loc[train_idx, '1step_train28e'] = df.loc[train_idx, '1step_train28e'].shift(-1)
df.loc[test_idx, '1step_test29e'] = df.loc[test_idx, '1step_test29e'].shift(-1)
df.loc[train_idx, '1step_train29e'] = df.loc[train_idx, '1step_train29e'].shift(-1)
df.loc[test_idx, '1step_test30e'] = df.loc[test_idx, '1step_test30e'].shift(-1)
df.loc[train_idx, '1step_train30e'] = df.loc[train_idx, '1step_train30e'].shift(-1)
df.loc[test_idx, '1step_test31e'] = df.loc[test_idx, '1step_test31e'].shift(-1)
df.loc[train_idx, '1step_train31e'] = df.loc[train_idx, '1step_train31e'].shift(-1)
df.loc[test_idx, '1step_test32e'] = df.loc[test_idx, '1step_test32e'].shift(-1)
df.loc[train_idx, '1step_train32e'] = df.loc[train_idx, '1step_train32e'].shift(-1)
df.loc[test_idx, '1step_test33e'] = df.loc[test_idx, '1step_test33e'].shift(-1)
df.loc[train_idx, '1step_train33e'] = df.loc[train_idx, '1step_train33e'].shift(-1)
df.loc[test_idx, '1step_test34e'] = df.loc[test_idx, '1step_test34e'].shift(-1)
df.loc[train_idx, '1step_train34e'] = df.loc[train_idx, '1step_train34e'].shift(-1)
df.loc[test_idx, '1step_test35e'] = df.loc[test_idx, '1step_test35e'].shift(-1)
df.loc[train_idx, '1step_train35e'] = df.loc[train_idx, '1step_train35e'].shift(-1)
df.loc[test_idx, '1step_test36e'] = df.loc[test_idx, '1step_test36e'].shift(-1)
df.loc[train_idx, '1step_train36e'] = df.loc[train_idx, '1step_train36e'].shift(-1)
#Regreso los datos diferenciados a su fecha original 2 LSTM 2 Capas
df.loc[test_idx, '1step_test2f'] = df.loc[test_idx, '1step_test2f'].shift(-1)
df.loc[train_idx, '1step_train2f'] = df.loc[train_idx, '1step_train2f'].shift(-1)
df.loc[test_idx, '1step_test3f'] = df.loc[test_idx, '1step_test3f'].shift(-1)
df.loc[train_idx, '1step_train3f'] = df.loc[train_idx, '1step_train3f'].shift(-1)
df.loc[test_idx, '1step_test4f'] = df.loc[test_idx, '1step_test4f'].shift(-1)
df.loc[train_idx, '1step_train4f'] = df.loc[train_idx, '1step_train4f'].shift(-1)
df.loc[test_idx, '1step_test5f'] = df.loc[test_idx, '1step_test5f'].shift(-1)
df.loc[train_idx, '1step_train5f'] = df.loc[train_idx, '1step_train5f'].shift(-1)
df.loc[test_idx, '1step_test6f'] = df.loc[test_idx, '1step_test6f'].shift(-1)
df.loc[train_idx, '1step_train6f'] = df.loc[train_idx, '1step_train6f'].shift(-1)
df.loc[test_idx, '1step_test7f'] = df.loc[test_idx, '1step_test7f'].shift(-1)
df.loc[train_idx, '1step_train7f'] = df.loc[train_idx, '1step_train7f'].shift(-1)
df.loc[test_idx, '1step_test8f'] = df.loc[test_idx, '1step_test8f'].shift(-1)
df.loc[train_idx, '1step_train8f'] = df.loc[train_idx, '1step_train8f'].shift(-1)
df.loc[test_idx, '1step_test9f'] = df.loc[test_idx, '1step_test9f'].shift(-1)
df.loc[train_idx, '1step_train9f'] = df.loc[train_idx, '1step_train9f'].shift(-1)
df.loc[test_idx, '1step_test10f'] = df.loc[test_idx, '1step_test10f'].shift(-1)
df.loc[train_idx, '1step_train10f'] = df.loc[train_idx, '1step_train10f'].shift(-1)
df.loc[test_idx, '1step_test11f'] = df.loc[test_idx, '1step_test11f'].shift(-1)
df.loc[train_idx, '1step_train11f'] = df.loc[train_idx, '1step_train11f'].shift(-1)
df.loc[test_idx, '1step_test12f'] = df.loc[test_idx, '1step_test12f'].shift(-1)
df.loc[train_idx, '1step_train12f'] = df.loc[train_idx, '1step_train12f'].shift(-1)
df.loc[test_idx, '1step_test13f'] = df.loc[test_idx, '1step_test13f'].shift(-1)
df.loc[train_idx, '1step_train13f'] = df.loc[train_idx, '1step_train13f'].shift(-1)
df.loc[test_idx, '1step_test14f'] = df.loc[test_idx, '1step_test14f'].shift(-1)
df.loc[train_idx, '1step_train14f'] = df.loc[train_idx, '1step_train14f'].shift(-1)
df.loc[test_idx, '1step_test15f'] = df.loc[test_idx, '1step_test15f'].shift(-1)
df.loc[train_idx, '1step_train15f'] = df.loc[train_idx, '1step_train15f'].shift(-1)
df.loc[test_idx, '1step_test16f'] = df.loc[test_idx, '1step_test16f'].shift(-1)
df.loc[train_idx, '1step_train16f'] = df.loc[train_idx, '1step_train16f'].shift(-1)
df.loc[test_idx, '1step_test17f'] = df.loc[test_idx, '1step_test17f'].shift(-1)
df.loc[train_idx, '1step_train17f'] = df.loc[train_idx, '1step_train17f'].shift(-1)
df.loc[test_idx, '1step_test18f'] = df.loc[test_idx, '1step_test18f'].shift(-1)
df.loc[train_idx, '1step_train18f'] = df.loc[train_idx, '1step_train18f'].shift(-1)
df.loc[test_idx, '1step_test19f'] = df.loc[test_idx, '1step_test19f'].shift(-1)
df.loc[train_idx, '1step_train19f'] = df.loc[train_idx, '1step_train19f'].shift(-1)
df.loc[test_idx, '1step_test20f'] = df.loc[test_idx, '1step_test20f'].shift(-1)
df.loc[train_idx, '1step_train20f'] = df.loc[train_idx, '1step_train20f'].shift(-1)
df.loc[test_idx, '1step_test21f'] = df.loc[test_idx, '1step_test21f'].shift(-1)
df.loc[train_idx, '1step_train21f'] = df.loc[train_idx, '1step_train21f'].shift(-1)
df.loc[test_idx, '1step_test22f'] = df.loc[test_idx, '1step_test22f'].shift(-1)
df.loc[train_idx, '1step_train22f'] = df.loc[train_idx, '1step_train22f'].shift(-1)
df.loc[test_idx, '1step_test23f'] = df.loc[test_idx, '1step_test23f'].shift(-1)
df.loc[train_idx, '1step_train23f'] = df.loc[train_idx, '1step_train23f'].shift(-1)
df.loc[test_idx, '1step_test24f'] = df.loc[test_idx, '1step_test24f'].shift(-1)
df.loc[train_idx, '1step_train24f'] = df.loc[train_idx, '1step_train24f'].shift(-1)
df.loc[test_idx, '1step_test25f'] = df.loc[test_idx, '1step_test25f'].shift(-1)
df.loc[train_idx, '1step_train25f'] = df.loc[train_idx, '1step_train25f'].shift(-1)
df.loc[test_idx, '1step_test26f'] = df.loc[test_idx, '1step_test26f'].shift(-1)
df.loc[train_idx, '1step_train26f'] = df.loc[train_idx, '1step_train26f'].shift(-1)
df.loc[test_idx, '1step_test27f'] = df.loc[test_idx, '1step_test27f'].shift(-1)
df.loc[train_idx, '1step_train27f'] = df.loc[train_idx, '1step_train27f'].shift(-1)
df.loc[test_idx, '1step_test28f'] = df.loc[test_idx, '1step_test28f'].shift(-1)
df.loc[train_idx, '1step_train28f'] = df.loc[train_idx, '1step_train28f'].shift(-1)
df.loc[test_idx, '1step_test29f'] = df.loc[test_idx, '1step_test29f'].shift(-1)
df.loc[train_idx, '1step_train29f'] = df.loc[train_idx, '1step_train29f'].shift(-1)
df.loc[test_idx, '1step_test30f'] = df.loc[test_idx, '1step_test30f'].shift(-1)
df.loc[train_idx, '1step_train30f'] = df.loc[train_idx, '1step_train30f'].shift(-1)
df.loc[test_idx, '1step_test31f'] = df.loc[test_idx, '1step_test31f'].shift(-1)
df.loc[train_idx, '1step_train31f'] = df.loc[train_idx, '1step_train31f'].shift(-1)
df.loc[test_idx, '1step_test32f'] = df.loc[test_idx, '1step_test32f'].shift(-1)
df.loc[train_idx, '1step_train32f'] = df.loc[train_idx, '1step_train32f'].shift(-1)
df.loc[test_idx, '1step_test33f'] = df.loc[test_idx, '1step_test33f'].shift(-1)
df.loc[train_idx, '1step_train33f'] = df.loc[train_idx, '1step_train33f'].shift(-1)
df.loc[test_idx, '1step_test34f'] = df.loc[test_idx, '1step_test34f'].shift(-1)
df.loc[train_idx, '1step_train34f'] = df.loc[train_idx, '1step_train34f'].shift(-1)
df.loc[test_idx, '1step_test35f'] = df.loc[test_idx, '1step_test35f'].shift(-1)
df.loc[train_idx, '1step_train35f'] = df.loc[train_idx, '1step_train35f'].shift(-1)
df.loc[test_idx, '1step_test36f'] = df.loc[test_idx, '1step_test36f'].shift(-1)
df.loc[train_idx, '1step_train36f'] = df.loc[train_idx, '1step_train36f'].shift(-1)
#Al regresar los datos se pierde un dato al final, se añade aquí: RNN 1 CAPA
last_forecast_test_2 = prev[test_idx][-2] + Ptest2[-1]
df.loc[test_idx, '1step_test2'] = df.loc[test_idx, '1step_test2'].replace(np.nan, last_forecast_test_2)
last_forecast_train_2 = prev[train_idx][-2] + Ptrain2[-1]
df.loc[train_idx, '1step_test2'] = df.loc[train_idx, '1step_train2'].replace(np.nan, last_forecast_train_2)
last_forecast_test_3 = prev[test_idx][-2] + Ptest3[-1]
df.loc[test_idx, '1step_test3'] = df.loc[test_idx, '1step_test3'].replace(np.nan, last_forecast_test_3)
last_forecast_train_3 = prev[train_idx][-2] + Ptrain3[-1]
df.loc[train_idx, '1step_test3'] = df.loc[train_idx, '1step_train3'].replace(np.nan, last_forecast_train_3)
last_forecast_test_4 = prev[test_idx][-2] + Ptest4[-1]
df.loc[test_idx, '1step_test4'] = df.loc[test_idx, '1step_test4'].replace(np.nan, last_forecast_test_4)
last_forecast_train_4 = prev[train_idx][-2] + Ptrain4[-1]
df.loc[train_idx, '1step_test4'] = df.loc[train_idx, '1step_train4'].replace(np.nan, last_forecast_train_4)
last_forecast_test_5 = prev[test_idx][-2] + Ptest5[-1]
df.loc[test_idx, '1step_test5'] = df.loc[test_idx, '1step_test5'].replace(np.nan, last_forecast_test_5)
last_forecast_train_5 = prev[train_idx][-2] + Ptrain5[-1]
df.loc[train_idx, '1step_test5'] = df.loc[train_idx, '1step_train5'].replace(np.nan, last_forecast_train_5)
last_forecast_test_6 = prev[test_idx][-2] + Ptest6[-1]
df.loc[test_idx, '1step_test6'] = df.loc[test_idx, '1step_test6'].replace(np.nan, last_forecast_test_6)
last_forecast_train_6 = prev[train_idx][-2] + Ptrain6[-1]
df.loc[train_idx, '1step_test6'] = df.loc[train_idx, '1step_train6'].replace(np.nan, last_forecast_train_6)
last_forecast_test_7 = prev[test_idx][-2] + Ptest7[-1]
df.loc[test_idx, '1step_test7'] = df.loc[test_idx, '1step_test7'].replace(np.nan, last_forecast_test_7)
last_forecast_train_7 = prev[train_idx][-2] + Ptrain7[-1]
df.loc[train_idx, '1step_test7'] = df.loc[train_idx, '1step_train7'].replace(np.nan, last_forecast_train_7)
last_forecast_test_8 = prev[test_idx][-2] + Ptest8[-1]
df.loc[test_idx, '1step_test8'] = df.loc[test_idx, '1step_test8'].replace(np.nan, last_forecast_test_8)
last_forecast_train_8 = prev[train_idx][-2] + Ptrain8[-1]
df.loc[train_idx, '1step_test8'] = df.loc[train_idx, '1step_train8'].replace(np.nan, last_forecast_train_8)
last_forecast_test_9 = prev[test_idx][-2] + Ptest9[-1]
df.loc[test_idx, '1step_test9'] = df.loc[test_idx, '1step_test9'].replace(np.nan, last_forecast_test_9)
last_forecast_train_9 = prev[train_idx][-2] + Ptrain9[-1]
df.loc[train_idx, '1step_test9'] = df.loc[train_idx, '1step_train9'].replace(np.nan, last_forecast_train_9)
last_forecast_test_10 = prev[test_idx][-2] + Ptest10[-1]
df.loc[test_idx, '1step_test10'] = df.loc[test_idx, '1step_test10'].replace(np.nan, last_forecast_test_10)
last_forecast_train_10 = prev[train_idx][-2] + Ptrain10[-1]
df.loc[train_idx, '1step_test10'] = df.loc[train_idx, '1step_train10'].replace(np.nan, last_forecast_train_10)
last_forecast_test_11 = prev[test_idx][-2] + Ptest11[-1]
df.loc[test_idx, '1step_test11'] = df.loc[test_idx, '1step_test11'].replace(np.nan, last_forecast_test_11)
last_forecast_train_11 = prev[train_idx][-2] + Ptrain11[-1]
df.loc[train_idx, '1step_test11'] = df.loc[train_idx, '1step_train11'].replace(np.nan, last_forecast_train_11)
last_forecast_test_12 = prev[test_idx][-2] + Ptest12[-1]
df.loc[test_idx, '1step_test12'] = df.loc[test_idx, '1step_test12'].replace(np.nan, last_forecast_test_12)
last_forecast_train_12 = prev[train_idx][-2] + Ptrain12[-1]
df.loc[train_idx, '1step_test12'] = df.loc[train_idx, '1step_train12'].replace(np.nan, last_forecast_train_12)
last_forecast_test_13 = prev[test_idx][-2] + Ptest13[-1]
df.loc[test_idx, '1step_test13'] = df.loc[test_idx, '1step_test13'].replace(np.nan, last_forecast_test_13)
last_forecast_train_13 = prev[train_idx][-2] + Ptrain13[-1]
df.loc[train_idx, '1step_test13'] = df.loc[train_idx, '1step_train13'].replace(np.nan, last_forecast_train_13)
last_forecast_test_14 = prev[test_idx][-2] + Ptest14[-1]
df.loc[test_idx, '1step_test14'] = df.loc[test_idx, '1step_test14'].replace(np.nan, last_forecast_test_14)
last_forecast_train_14 = prev[train_idx][-2] + Ptrain14[-1]
df.loc[train_idx, '1step_test14'] = df.loc[train_idx, '1step_train14'].replace(np.nan, last_forecast_train_14)
last_forecast_test_15 = prev[test_idx][-2] + Ptest15[-1]
df.loc[test_idx, '1step_test15'] = df.loc[test_idx, '1step_test15'].replace(np.nan, last_forecast_test_15)
last_forecast_train_15 = prev[train_idx][-2] + Ptrain15[-1]
df.loc[train_idx, '1step_test15'] = df.loc[train_idx, '1step_train15'].replace(np.nan, last_forecast_train_15)
last_forecast_test_16 = prev[test_idx][-2] + Ptest16[-1]
df.loc[test_idx, '1step_test16'] = df.loc[test_idx, '1step_test16'].replace(np.nan, last_forecast_test_16)
last_forecast_train_16 = prev[train_idx][-2] + Ptrain16[-1]
df.loc[train_idx, '1step_test16'] = df.loc[train_idx, '1step_train16'].replace(np.nan, last_forecast_train_16)
last_forecast_test_17 = prev[test_idx][-2] + Ptest17[-1]
df.loc[test_idx, '1step_test17'] = df.loc[test_idx, '1step_test17'].replace(np.nan, last_forecast_test_17)
last_forecast_train_17 = prev[train_idx][-2] + Ptrain17[-1]
df.loc[train_idx, '1step_test17'] = df.loc[train_idx, '1step_train17'].replace(np.nan, last_forecast_train_17)
last_forecast_test_18 = prev[test_idx][-2] + Ptest18[-1]
df.loc[test_idx, '1step_test18'] = df.loc[test_idx, '1step_test18'].replace(np.nan, last_forecast_test_18)
last_forecast_train_18 = prev[train_idx][-2] + Ptrain18[-1]
df.loc[train_idx, '1step_test18'] = df.loc[train_idx, '1step_train18'].replace(np.nan, last_forecast_train_18)
last_forecast_test_19 = prev[test_idx][-2] + Ptest19[-1]
df.loc[test_idx, '1step_test19'] = df.loc[test_idx, '1step_test19'].replace(np.nan, last_forecast_test_19)
last_forecast_train_19 = prev[train_idx][-2] + Ptrain19[-1]
df.loc[train_idx, '1step_test19'] = df.loc[train_idx, '1step_train19'].replace(np.nan, last_forecast_train_19)
last_forecast_test_20 = prev[test_idx][-2] + Ptest20[-1]
df.loc[test_idx, '1step_test20'] = df.loc[test_idx, '1step_test20'].replace(np.nan, last_forecast_test_20)
last_forecast_train_20 = prev[train_idx][-2] + Ptrain20[-1]
df.loc[train_idx, '1step_test20'] = df.loc[train_idx, '1step_train20'].replace(np.nan, last_forecast_train_20)
last_forecast_test_21 = prev[test_idx][-2] + Ptest21[-1]
df.loc[test_idx, '1step_test21'] = df.loc[test_idx, '1step_test21'].replace(np.nan, last_forecast_test_21)
last_forecast_train_21 = prev[train_idx][-2] + Ptrain21[-1]
df.loc[train_idx, '1step_test21'] = df.loc[train_idx, '1step_train21'].replace(np.nan, last_forecast_train_21)
last_forecast_test_22 = prev[test_idx][-2] + Ptest22[-1]
df.loc[test_idx, '1step_test22'] = df.loc[test_idx, '1step_test22'].replace(np.nan, last_forecast_test_22)
last_forecast_train_22 = prev[train_idx][-2] + Ptrain22[-1]
df.loc[train_idx, '1step_test22'] = df.loc[train_idx, '1step_train22'].replace(np.nan, last_forecast_train_22)
last_forecast_test_23 = prev[test_idx][-2] + Ptest23[-1]
df.loc[test_idx, '1step_test23'] = df.loc[test_idx, '1step_test23'].replace(np.nan, last_forecast_test_23)
last_forecast_train_23 = prev[train_idx][-2] + Ptrain23[-1]
df.loc[train_idx, '1step_test23'] = df.loc[train_idx, '1step_train23'].replace(np.nan, last_forecast_train_23)
last_forecast_test_24 = prev[test_idx][-2] + Ptest24[-1]
df.loc[test_idx, '1step_test24'] = df.loc[test_idx, '1step_test24'].replace(np.nan, last_forecast_test_24)
last_forecast_train_24 = prev[train_idx][-2] + Ptrain24[-1]
df.loc[train_idx, '1step_test24'] = df.loc[train_idx, '1step_train24'].replace(np.nan, last_forecast_train_24)
last_forecast_test_25 = prev[test_idx][-2] + Ptest25[-1]
df.loc[test_idx, '1step_test25'] = df.loc[test_idx, '1step_test25'].replace(np.nan, last_forecast_test_25)
last_forecast_train_25 = prev[train_idx][-2] + Ptrain25[-1]
df.loc[train_idx, '1step_test25'] = df.loc[train_idx, '1step_train25'].replace(np.nan, last_forecast_train_25)
last_forecast_test_26 = prev[test_idx][-2] + Ptest26[-1]
df.loc[test_idx, '1step_test26'] = df.loc[test_idx, '1step_test26'].replace(np.nan, last_forecast_test_26)
last_forecast_train_26 = prev[train_idx][-2] + Ptrain26[-1]
df.loc[train_idx, '1step_test26'] = df.loc[train_idx, '1step_train26'].replace(np.nan, last_forecast_train_26)
last_forecast_test_27 = prev[test_idx][-2] + Ptest27[-1]
df.loc[test_idx, '1step_test27'] = df.loc[test_idx, '1step_test27'].replace(np.nan, last_forecast_test_27)
last_forecast_train_27 = prev[train_idx][-2] + Ptrain27[-1]
df.loc[train_idx, '1step_test27'] = df.loc[train_idx, '1step_train27'].replace(np.nan, last_forecast_train_27)
last_forecast_test_28 = prev[test_idx][-2] + Ptest28[-1]
df.loc[test_idx, '1step_test28'] = df.loc[test_idx, '1step_test28'].replace(np.nan, last_forecast_test_28)
last_forecast_train_28 = prev[train_idx][-2] + Ptrain28[-1]
df.loc[train_idx, '1step_test28'] = df.loc[train_idx, '1step_train28'].replace(np.nan, last_forecast_train_28)
last_forecast_test_29 = prev[test_idx][-2] + Ptest29[-1]
df.loc[test_idx, '1step_test29'] = df.loc[test_idx, '1step_test29'].replace(np.nan, last_forecast_test_29)
last_forecast_train_29 = prev[train_idx][-2] + Ptrain29[-1]
df.loc[train_idx, '1step_test29'] = df.loc[train_idx, '1step_train29'].replace(np.nan, last_forecast_train_29)
last_forecast_test_30 = prev[test_idx][-2] + Ptest30[-1]
df.loc[test_idx, '1step_test30'] = df.loc[test_idx, '1step_test30'].replace(np.nan, last_forecast_test_30)
last_forecast_train_30 = prev[train_idx][-2] + Ptrain30[-1]
df.loc[train_idx, '1step_test30'] = df.loc[train_idx, '1step_train30'].replace(np.nan, last_forecast_train_30)
last_forecast_test_31 = prev[test_idx][-2] + Ptest31[-1]
df.loc[test_idx, '1step_test31'] = df.loc[test_idx, '1step_test31'].replace(np.nan, last_forecast_test_31)
last_forecast_train_31 = prev[train_idx][-2] + Ptrain31[-1]
df.loc[train_idx, '1step_test31'] = df.loc[train_idx, '1step_train31'].replace(np.nan, last_forecast_train_31)
last_forecast_test_32 = prev[test_idx][-2] + Ptest32[-1]
df.loc[test_idx, '1step_test32'] = df.loc[test_idx, '1step_test32'].replace(np.nan, last_forecast_test_32)
last_forecast_train_32 = prev[train_idx][-2] + Ptrain32[-1]
df.loc[train_idx, '1step_test32'] = df.loc[train_idx, '1step_train32'].replace(np.nan, last_forecast_train_32)
last_forecast_test_33 = prev[test_idx][-2] + Ptest33[-1]
df.loc[test_idx, '1step_test33'] = df.loc[test_idx, '1step_test33'].replace(np.nan, last_forecast_test_33)
last_forecast_train_33 = prev[train_idx][-2] + Ptrain33[-1]
df.loc[train_idx, '1step_test33'] = df.loc[train_idx, '1step_train33'].replace(np.nan, last_forecast_train_33)
last_forecast_test_34 = prev[test_idx][-2] + Ptest34[-1]
df.loc[test_idx, '1step_test34'] = df.loc[test_idx, '1step_test34'].replace(np.nan, last_forecast_test_34)
last_forecast_train_34 = prev[train_idx][-2] + Ptrain34[-1]
df.loc[train_idx, '1step_test34'] = df.loc[train_idx, '1step_train34'].replace(np.nan, last_forecast_train_34)
last_forecast_test_35 = prev[test_idx][-2] + Ptest35[-1]
df.loc[test_idx, '1step_test35'] = df.loc[test_idx, '1step_test35'].replace(np.nan, last_forecast_test_35)
last_forecast_train_35 = prev[train_idx][-2] + Ptrain35[-1]
df.loc[train_idx, '1step_test35'] = df.loc[train_idx, '1step_train35'].replace(np.nan, last_forecast_train_35)
last_forecast_test_36 = prev[test_idx][-2] + Ptest36[-1]
df.loc[test_idx, '1step_test36'] = df.loc[test_idx, '1step_test36'].replace(np.nan, last_forecast_test_36)
last_forecast_train_36 = prev[train_idx][-2] + Ptrain36[-1]
df.loc[train_idx, '1step_test36'] = df.loc[train_idx, '1step_train36'].replace(np.nan, last_forecast_train_36)
#Al regresar los datos se pierde un dato al final, se añade aquí: RNN 2 CAPAS
last_forecast_test_2b = prev[test_idx][-2] + Ptest2b[-1]
df.loc[test_idx, '1step_test2b'] = df.loc[test_idx, '1step_test2b'].replace(np.nan, last_forecast_test_2b)
last_forecast_train_2b = prev[train_idx][-2] + Ptrain2b[-1]
df.loc[train_idx, '1step_test2b'] = df.loc[train_idx, '1step_train2b'].replace(np.nan, last_forecast_train_2b)
last_forecast_test_3b = prev[test_idx][-2] + Ptest3b[-1]
df.loc[test_idx, '1step_test3b'] = df.loc[test_idx, '1step_test3b'].replace(np.nan, last_forecast_test_3b)
last_forecast_train_3b= prev[train_idx][-2] + Ptrain3b[-1]
df.loc[train_idx, '1step_test3b'] = df.loc[train_idx, '1step_train3b'].replace(np.nan, last_forecast_train_3b)
last_forecast_test_4b = prev[test_idx][-2] + Ptest4b[-1]
df.loc[test_idx, '1step_test4b'] = df.loc[test_idx, '1step_test4b'].replace(np.nan, last_forecast_test_4b)
last_forecast_train_4b = prev[train_idx][-2] + Ptrain4b[-1]
df.loc[train_idx, '1step_test4b'] = df.loc[train_idx, '1step_train4b'].replace(np.nan, last_forecast_train_4b)
last_forecast_test_5b = prev[test_idx][-2] + Ptest5b[-1]
df.loc[test_idx, '1step_test5b'] = df.loc[test_idx, '1step_test5b'].replace(np.nan, last_forecast_test_5b)
last_forecast_train_5b = prev[train_idx][-2] + Ptrain5b[-1]
df.loc[train_idx, '1step_test5b'] = df.loc[train_idx, '1step_train5b'].replace(np.nan, last_forecast_train_5b)
last_forecast_test_6b = prev[test_idx][-2] + Ptest6b[-1]
df.loc[test_idx, '1step_test6b'] = df.loc[test_idx, '1step_test6b'].replace(np.nan, last_forecast_test_6b)
last_forecast_train_6b = prev[train_idx][-2] + Ptrain6b[-1]
df.loc[train_idx, '1step_test6b'] = df.loc[train_idx, '1step_train6b'].replace(np.nan, last_forecast_train_6b)
last_forecast_test_7b = prev[test_idx][-2] + Ptest7b[-1]
df.loc[test_idx, '1step_test7b'] = df.loc[test_idx, '1step_test7b'].replace(np.nan, last_forecast_test_7b)
last_forecast_train_7b = prev[train_idx][-2] + Ptrain7b[-1]
df.loc[train_idx, '1step_test7b'] = df.loc[train_idx, '1step_train7b'].replace(np.nan, last_forecast_train_7b)
last_forecast_test_8b = prev[test_idx][-2] + Ptest8b[-1]
df.loc[test_idx, '1step_test8b'] = df.loc[test_idx, '1step_test8b'].replace(np.nan, last_forecast_test_8b)
last_forecast_train_8b = prev[train_idx][-2] + Ptrain8b[-1]
df.loc[train_idx, '1step_test8b'] = df.loc[train_idx, '1step_train8b'].replace(np.nan, last_forecast_train_8b)
last_forecast_test_9b = prev[test_idx][-2] + Ptest9b[-1]
df.loc[test_idx, '1step_test9b'] = df.loc[test_idx, '1step_test9b'].replace(np.nan, last_forecast_test_9b)
last_forecast_train_9b = prev[train_idx][-2] + Ptrain9b[-1]
df.loc[train_idx, '1step_test9b'] = df.loc[train_idx, '1step_train9b'].replace(np.nan, last_forecast_train_9b)
last_forecast_test_10b = prev[test_idx][-2] + Ptest10b[-1]
df.loc[test_idx, '1step_test10b'] = df.loc[test_idx, '1step_test10b'].replace(np.nan, last_forecast_test_10b)
last_forecast_train_10b = prev[train_idx][-2] + Ptrain10b[-1]
df.loc[train_idx, '1step_test10b'] = df.loc[train_idx, '1step_train10b'].replace(np.nan, last_forecast_train_10b)
last_forecast_test_11b = prev[test_idx][-2] + Ptest11b[-1]
df.loc[test_idx, '1step_test11b'] = df.loc[test_idx, '1step_test11b'].replace(np.nan, last_forecast_test_11b)
last_forecast_train_11b = prev[train_idx][-2] + Ptrain11b[-1]
df.loc[train_idx, '1step_test11b'] = df.loc[train_idx, '1step_train11b'].replace(np.nan, last_forecast_train_11b)
last_forecast_test_12b = prev[test_idx][-2] + Ptest12b[-1]
df.loc[test_idx, '1step_test12b'] = df.loc[test_idx, '1step_test12b'].replace(np.nan, last_forecast_test_12b)
last_forecast_train_12b = prev[train_idx][-2] + Ptrain12b[-1]
df.loc[train_idx, '1step_test12b'] = df.loc[train_idx, '1step_train12b'].replace(np.nan, last_forecast_train_12b)
last_forecast_test_13b = prev[test_idx][-2] + Ptest13b[-1]
df.loc[test_idx, '1step_test13b'] = df.loc[test_idx, '1step_test13b'].replace(np.nan, last_forecast_test_13b)
last_forecast_train_13b = prev[train_idx][-2] + Ptrain13b[-1]
df.loc[train_idx, '1step_test13b'] = df.loc[train_idx, '1step_train13b'].replace(np.nan, last_forecast_train_13b)
last_forecast_test_14b = prev[test_idx][-2] + Ptest14b[-1]
df.loc[test_idx, '1step_test14b'] = df.loc[test_idx, '1step_test14b'].replace(np.nan, last_forecast_test_14b)
last_forecast_train_14b = prev[train_idx][-2] + Ptrain14b[-1]
df.loc[train_idx, '1step_test14b'] = df.loc[train_idx, '1step_train14b'].replace(np.nan, last_forecast_train_14b)
last_forecast_test_15b = prev[test_idx][-2] + Ptest15b[-1]
df.loc[test_idx, '1step_test15b'] = df.loc[test_idx, '1step_test15b'].replace(np.nan, last_forecast_test_15b)
last_forecast_train_15b = prev[train_idx][-2] + Ptrain15b[-1]
df.loc[train_idx, '1step_test15b'] = df.loc[train_idx, '1step_train15b'].replace(np.nan, last_forecast_train_15b)
last_forecast_test_16b = prev[test_idx][-2] + Ptest16b[-1]
df.loc[test_idx, '1step_test16b'] = df.loc[test_idx, '1step_test16b'].replace(np.nan, last_forecast_test_16b)
last_forecast_train_16b= prev[train_idx][-2] + Ptrain16b[-1]
df.loc[train_idx, '1step_test16b'] = df.loc[train_idx, '1step_train16b'].replace(np.nan, last_forecast_train_16b)
last_forecast_test_17b = prev[test_idx][-2] + Ptest17b[-1]
df.loc[test_idx, '1step_test17b'] = df.loc[test_idx, '1step_test17b'].replace(np.nan, last_forecast_test_17b)
last_forecast_train_17b = prev[train_idx][-2] + Ptrain17b[-1]
df.loc[train_idx, '1step_test17b'] = df.loc[train_idx, '1step_train17b'].replace(np.nan, last_forecast_train_17b)
last_forecast_test_18b = prev[test_idx][-2] + Ptest18b[-1]
df.loc[test_idx, '1step_test18b'] = df.loc[test_idx, '1step_test18b'].replace(np.nan, last_forecast_test_18b)
last_forecast_train_18b = prev[train_idx][-2] + Ptrain18b[-1]
df.loc[train_idx, '1step_test18b'] = df.loc[train_idx, '1step_train18b'].replace(np.nan, last_forecast_train_18b)
last_forecast_test_19b = prev[test_idx][-2] + Ptest19b[-1]
df.loc[test_idx, '1step_test19b'] = df.loc[test_idx, '1step_test19b'].replace(np.nan, last_forecast_test_19b)
last_forecast_train_19b = prev[train_idx][-2] + Ptrain19b[-1]
df.loc[train_idx, '1step_test19b'] = df.loc[train_idx, '1step_train19b'].replace(np.nan, last_forecast_train_19b)
last_forecast_test_20b = prev[test_idx][-2] + Ptest20b[-1]
df.loc[test_idx, '1step_test20b'] = df.loc[test_idx, '1step_test20b'].replace(np.nan, last_forecast_test_20b)
last_forecast_train_20b = prev[train_idx][-2] + Ptrain20b[-1]
df.loc[train_idx, '1step_test20b'] = df.loc[train_idx, '1step_train20b'].replace(np.nan, last_forecast_train_20b)
last_forecast_test_21b = prev[test_idx][-2] + Ptest21b[-1]
df.loc[test_idx, '1step_test21b'] = df.loc[test_idx, '1step_test21b'].replace(np.nan, last_forecast_test_21b)
last_forecast_train_21b = prev[train_idx][-2] + Ptrain21b[-1]
df.loc[train_idx, '1step_test21b'] = df.loc[train_idx, '1step_train21b'].replace(np.nan, last_forecast_train_21b)
last_forecast_test_22b = prev[test_idx][-2] + Ptest22b[-1]
df.loc[test_idx, '1step_test22b'] = df.loc[test_idx, '1step_test22b'].replace(np.nan, last_forecast_test_22b)
last_forecast_train_22b = prev[train_idx][-2] + Ptrain22b[-1]
df.loc[train_idx, '1step_test22b'] = df.loc[train_idx, '1step_train22b'].replace(np.nan, last_forecast_train_22b)
last_forecast_test_23b = prev[test_idx][-2] + Ptest23b[-1]
df.loc[test_idx, '1step_test23b'] = df.loc[test_idx, '1step_test23b'].replace(np.nan, last_forecast_test_23b)
last_forecast_train_23b = prev[train_idx][-2] + Ptrain23b[-1]
df.loc[train_idx, '1step_test23b'] = df.loc[train_idx, '1step_train23b'].replace(np.nan, last_forecast_train_23b)
last_forecast_test_24b = prev[test_idx][-2] + Ptest24b[-1]
df.loc[test_idx, '1step_test24b'] = df.loc[test_idx, '1step_test24b'].replace(np.nan, last_forecast_test_24b)
last_forecast_train_24b = prev[train_idx][-2] + Ptrain24b[-1]
df.loc[train_idx, '1step_test24b'] = df.loc[train_idx, '1step_train24b'].replace(np.nan, last_forecast_train_24b)
last_forecast_test_25b = prev[test_idx][-2] + Ptest25b[-1]
df.loc[test_idx, '1step_test25b'] = df.loc[test_idx, '1step_test25b'].replace(np.nan, last_forecast_test_25b)
last_forecast_train_25b = prev[train_idx][-2] + Ptrain25b[-1]
df.loc[train_idx, '1step_test25b'] = df.loc[train_idx, '1step_train25b'].replace(np.nan, last_forecast_train_25b)
last_forecast_test_26b = prev[test_idx][-2] + Ptest26b[-1]
df.loc[test_idx, '1step_test26b'] = df.loc[test_idx, '1step_test26b'].replace(np.nan, last_forecast_test_26b)
last_forecast_train_26b = prev[train_idx][-2] + Ptrain26b[-1]
df.loc[train_idx, '1step_test26b'] = df.loc[train_idx, '1step_train26b'].replace(np.nan, last_forecast_train_26b)
last_forecast_test_27b = prev[test_idx][-2] + Ptest27b[-1]
df.loc[test_idx, '1step_test27b'] = df.loc[test_idx, '1step_test27b'].replace(np.nan, last_forecast_test_27b)
last_forecast_train_27b = prev[train_idx][-2] + Ptrain27b[-1]
df.loc[train_idx, '1step_test27b'] = df.loc[train_idx, '1step_train27b'].replace(np.nan, last_forecast_train_27b)
last_forecast_test_28b = prev[test_idx][-2] + Ptest28b[-1]
df.loc[test_idx, '1step_test28b'] = df.loc[test_idx, '1step_test28b'].replace(np.nan, last_forecast_test_28b)
last_forecast_train_28b = prev[train_idx][-2] + Ptrain28b[-1]
df.loc[train_idx, '1step_test28b'] = df.loc[train_idx, '1step_train28b'].replace(np.nan, last_forecast_train_28b)
last_forecast_test_29b = prev[test_idx][-2] + Ptest29b[-1]
df.loc[test_idx, '1step_test29b'] = df.loc[test_idx, '1step_test29b'].replace(np.nan, last_forecast_test_29b)
last_forecast_train_29b = prev[train_idx][-2] + Ptrain29b[-1]
df.loc[train_idx, '1step_test29b'] = df.loc[train_idx, '1step_train29b'].replace(np.nan, last_forecast_train_29b)
last_forecast_test_30b = prev[test_idx][-2] + Ptest30b[-1]
df.loc[test_idx, '1step_test30b'] = df.loc[test_idx, '1step_test30b'].replace(np.nan, last_forecast_test_30b)
last_forecast_train_30b = prev[train_idx][-2] + Ptrain30b[-1]
df.loc[train_idx, '1step_test30b'] = df.loc[train_idx, '1step_train30b'].replace(np.nan, last_forecast_train_30b)
last_forecast_test_31b = prev[test_idx][-2] + Ptest31b[-1]
df.loc[test_idx, '1step_test31b'] = df.loc[test_idx, '1step_test31b'].replace(np.nan, last_forecast_test_31b)
last_forecast_train_31b = prev[train_idx][-2] + Ptrain31b[-1]
df.loc[train_idx, '1step_test31b'] = df.loc[train_idx, '1step_train31b'].replace(np.nan, last_forecast_train_31b)
last_forecast_test_32b = prev[test_idx][-2] + Ptest32b[-1]
df.loc[test_idx, '1step_test32b'] = df.loc[test_idx, '1step_test32b'].replace(np.nan, last_forecast_test_32b)
last_forecast_train_32b = prev[train_idx][-2] + Ptrain32b[-1]
df.loc[train_idx, '1step_test32b'] = df.loc[train_idx, '1step_train32b'].replace(np.nan, last_forecast_train_32b)
last_forecast_test_33b = prev[test_idx][-2] + Ptest33b[-1]
df.loc[test_idx, '1step_test33b'] = df.loc[test_idx, '1step_test33b'].replace(np.nan, last_forecast_test_33b)
last_forecast_train_33b = prev[train_idx][-2] + Ptrain33b[-1]
df.loc[train_idx, '1step_test33b'] = df.loc[train_idx, '1step_train33b'].replace(np.nan, last_forecast_train_33b)
last_forecast_test_34b = prev[test_idx][-2] + Ptest34b[-1]
df.loc[test_idx, '1step_test34b'] = df.loc[test_idx, '1step_test34b'].replace(np.nan, last_forecast_test_34b)
last_forecast_train_34b = prev[train_idx][-2] + Ptrain34b[-1]
df.loc[train_idx, '1step_test34b'] = df.loc[train_idx, '1step_train34b'].replace(np.nan, last_forecast_train_34b)
last_forecast_test_35b = prev[test_idx][-2] + Ptest35b[-1]
df.loc[test_idx, '1step_test35b'] = df.loc[test_idx, '1step_test35b'].replace(np.nan, last_forecast_test_35b)
last_forecast_train_35b = prev[train_idx][-2] + Ptrain35b[-1]
df.loc[train_idx, '1step_test35b'] = df.loc[train_idx, '1step_train35b'].replace(np.nan, last_forecast_train_35b)
last_forecast_test_36b = prev[test_idx][-2] + Ptest36b[-1]
df.loc[test_idx, '1step_test36b'] = df.loc[test_idx, '1step_test36b'].replace(np.nan, last_forecast_test_36b)
last_forecast_train_36b = prev[train_idx][-2] + Ptrain36b[-1]
df.loc[train_idx, '1step_test36b'] = df.loc[train_idx, '1step_train36b'].replace(np.nan, last_forecast_train_36b)
#Al regresar los datos se pierde un dato al final, se añade aquí: LSTM 1 CAPA
last_forecast_test_2c = prev[test_idx][-2] + Ptest2c[-1]
df.loc[test_idx, '1step_test2c'] = df.loc[test_idx, '1step_test2c'].replace(np.nan, last_forecast_test_2c)
last_forecast_train_2c = prev[train_idx][-2] + Ptrain2c[-1]
df.loc[train_idx, '1step_test2c'] = df.loc[train_idx, '1step_train2c'].replace(np.nan, last_forecast_train_2c)
last_forecast_test_3c = prev[test_idx][-2] + Ptest3c[-1]
df.loc[test_idx, '1step_test3c'] = df.loc[test_idx, '1step_test3c'].replace(np.nan, last_forecast_test_3c)
last_forecast_train_3c= prev[train_idx][-2] + Ptrain3c[-1]
df.loc[train_idx, '1step_test3c'] = df.loc[train_idx, '1step_train3c'].replace(np.nan, last_forecast_train_3c)
last_forecast_test_4c = prev[test_idx][-2] + Ptest4c[-1]
df.loc[test_idx, '1step_test4c'] = df.loc[test_idx, '1step_test4c'].replace(np.nan, last_forecast_test_4c)
last_forecast_train_4c = prev[train_idx][-2] + Ptrain4c[-1]
df.loc[train_idx, '1step_test4c'] = df.loc[train_idx, '1step_train4c'].replace(np.nan, last_forecast_train_4c)
last_forecast_test_5c = prev[test_idx][-2] + Ptest5c[-1]
df.loc[test_idx, '1step_test5c'] = df.loc[test_idx, '1step_test5c'].replace(np.nan, last_forecast_test_5c)
last_forecast_train_5c = prev[train_idx][-2] + Ptrain5c[-1]
df.loc[train_idx, '1step_test5c'] = df.loc[train_idx, '1step_train5c'].replace(np.nan, last_forecast_train_5c)
last_forecast_test_6c = prev[test_idx][-2] + Ptest6c[-1]
df.loc[test_idx, '1step_test6c'] = df.loc[test_idx, '1step_test6c'].replace(np.nan, last_forecast_test_6c)
last_forecast_train_6c = prev[train_idx][-2] + Ptrain6c[-1]
df.loc[train_idx, '1step_test6c'] = df.loc[train_idx, '1step_train6c'].replace(np.nan, last_forecast_train_6c)
last_forecast_test_7c = prev[test_idx][-2] + Ptest7c[-1]
df.loc[test_idx, '1step_test7c'] = df.loc[test_idx, '1step_test7c'].replace(np.nan, last_forecast_test_7c)
last_forecast_train_7c = prev[train_idx][-2] + Ptrain7c[-1]
df.loc[train_idx, '1step_test7c'] = df.loc[train_idx, '1step_train7c'].replace(np.nan, last_forecast_train_7c)
last_forecast_test_8c = prev[test_idx][-2] + Ptest8c[-1]
df.loc[test_idx, '1step_test8c'] = df.loc[test_idx, '1step_test8c'].replace(np.nan, last_forecast_test_8c)
last_forecast_train_8c = prev[train_idx][-2] + Ptrain8c[-1]
df.loc[train_idx, '1step_test8c'] = df.loc[train_idx, '1step_train8c'].replace(np.nan, last_forecast_train_8c)
last_forecast_test_9c = prev[test_idx][-2] + Ptest9c[-1]
df.loc[test_idx, '1step_test9c'] = df.loc[test_idx, '1step_test9c'].replace(np.nan, last_forecast_test_9c)
last_forecast_train_9c = prev[train_idx][-2] + Ptrain9c[-1]
df.loc[train_idx, '1step_test9c'] = df.loc[train_idx, '1step_train9c'].replace(np.nan, last_forecast_train_9c)
last_forecast_test_10c = prev[test_idx][-2] + Ptest10c[-1]
df.loc[test_idx, '1step_test10c'] = df.loc[test_idx, '1step_test10c'].replace(np.nan, last_forecast_test_10c)
last_forecast_train_10c = prev[train_idx][-2] + Ptrain10c[-1]
df.loc[train_idx, '1step_test10c'] = df.loc[train_idx, '1step_train10c'].replace(np.nan, last_forecast_train_10c)
last_forecast_test_11c = prev[test_idx][-2] + Ptest11c[-1]
df.loc[test_idx, '1step_test11c'] = df.loc[test_idx, '1step_test11c'].replace(np.nan, last_forecast_test_11c)
last_forecast_train_11c = prev[train_idx][-2] + Ptrain11c[-1]
df.loc[train_idx, '1step_test11c'] = df.loc[train_idx, '1step_train11c'].replace(np.nan, last_forecast_train_11c)
last_forecast_test_12c = prev[test_idx][-2] + Ptest12c[-1]
df.loc[test_idx, '1step_test12c'] = df.loc[test_idx, '1step_test12c'].replace(np.nan, last_forecast_test_12c)
last_forecast_train_12c = prev[train_idx][-2] + Ptrain12c[-1]
df.loc[train_idx, '1step_test12c'] = df.loc[train_idx, '1step_train12c'].replace(np.nan, last_forecast_train_12c)
last_forecast_test_13c = prev[test_idx][-2] + Ptest13c[-1]
df.loc[test_idx, '1step_test13c'] = df.loc[test_idx, '1step_test13c'].replace(np.nan, last_forecast_test_13c)
last_forecast_train_13c = prev[train_idx][-2] + Ptrain13c[-1]
df.loc[train_idx, '1step_test13c'] = df.loc[train_idx, '1step_train13c'].replace(np.nan, last_forecast_train_13c)
last_forecast_test_14c = prev[test_idx][-2] + Ptest14c[-1]
df.loc[test_idx, '1step_test14c'] = df.loc[test_idx, '1step_test14c'].replace(np.nan, last_forecast_test_14c)
last_forecast_train_14c = prev[train_idx][-2] + Ptrain14c[-1]
df.loc[train_idx, '1step_test14c'] = df.loc[train_idx, '1step_train14c'].replace(np.nan, last_forecast_train_14c)
last_forecast_test_15c = prev[test_idx][-2] + Ptest15c[-1]
df.loc[test_idx, '1step_test15c'] = df.loc[test_idx, '1step_test15c'].replace(np.nan, last_forecast_test_15c)
last_forecast_train_15c = prev[train_idx][-2] + Ptrain15c[-1]
df.loc[train_idx, '1step_test15c'] = df.loc[train_idx, '1step_train15c'].replace(np.nan, last_forecast_train_15c)
last_forecast_test_16c = prev[test_idx][-2] + Ptest16c[-1]
df.loc[test_idx, '1step_test16c'] = df.loc[test_idx, '1step_test16c'].replace(np.nan, last_forecast_test_16c)
last_forecast_train_16c= prev[train_idx][-2] + Ptrain16c[-1]
df.loc[train_idx, '1step_test16c'] = df.loc[train_idx, '1step_train16c'].replace(np.nan, last_forecast_train_16c)
last_forecast_test_17c = prev[test_idx][-2] + Ptest17c[-1]
df.loc[test_idx, '1step_test17c'] = df.loc[test_idx, '1step_test17c'].replace(np.nan, last_forecast_test_17c)
last_forecast_train_17c = prev[train_idx][-2] + Ptrain17c[-1]
df.loc[train_idx, '1step_test17c'] = df.loc[train_idx, '1step_train17c'].replace(np.nan, last_forecast_train_17c)
last_forecast_test_18c = prev[test_idx][-2] + Ptest18c[-1]
df.loc[test_idx, '1step_test18c'] = df.loc[test_idx, '1step_test18c'].replace(np.nan, last_forecast_test_18c)
last_forecast_train_18c = prev[train_idx][-2] + Ptrain18c[-1]
df.loc[train_idx, '1step_test18c'] = df.loc[train_idx, '1step_train18c'].replace(np.nan, last_forecast_train_18c)
last_forecast_test_19c = prev[test_idx][-2] + Ptest19c[-1]
df.loc[test_idx, '1step_test19c'] = df.loc[test_idx, '1step_test19c'].replace(np.nan, last_forecast_test_19c)
last_forecast_train_19c = prev[train_idx][-2] + Ptrain19c[-1]
df.loc[train_idx, '1step_test19c'] = df.loc[train_idx, '1step_train19c'].replace(np.nan, last_forecast_train_19c)
last_forecast_test_20c = prev[test_idx][-2] + Ptest20c[-1]
df.loc[test_idx, '1step_test20c'] = df.loc[test_idx, '1step_test20c'].replace(np.nan, last_forecast_test_20c)
last_forecast_train_20c = prev[train_idx][-2] + Ptrain20c[-1]
df.loc[train_idx, '1step_test20c'] = df.loc[train_idx, '1step_train20c'].replace(np.nan, last_forecast_train_20c)
last_forecast_test_21c = prev[test_idx][-2] + Ptest21c[-1]
df.loc[test_idx, '1step_test21c'] = df.loc[test_idx, '1step_test21c'].replace(np.nan, last_forecast_test_21c)
last_forecast_train_21c = prev[train_idx][-2] + Ptrain21c[-1]
df.loc[train_idx, '1step_test21c'] = df.loc[train_idx, '1step_train21c'].replace(np.nan, last_forecast_train_21c)
last_forecast_test_22c = prev[test_idx][-2] + Ptest22c[-1]
df.loc[test_idx, '1step_test22c'] = df.loc[test_idx, '1step_test22c'].replace(np.nan, last_forecast_test_22c)
last_forecast_train_22c = prev[train_idx][-2] + Ptrain22c[-1]
df.loc[train_idx, '1step_test22c'] = df.loc[train_idx, '1step_train22c'].replace(np.nan, last_forecast_train_22c)
last_forecast_test_23c = prev[test_idx][-2] + Ptest23c[-1]
df.loc[test_idx, '1step_test23c'] = df.loc[test_idx, '1step_test23c'].replace(np.nan, last_forecast_test_23c)
last_forecast_train_23c = prev[train_idx][-2] + Ptrain23c[-1]
df.loc[train_idx, '1step_test23c'] = df.loc[train_idx, '1step_train23c'].replace(np.nan, last_forecast_train_23c)
last_forecast_test_24c = prev[test_idx][-2] + Ptest24c[-1]
df.loc[test_idx, '1step_test24c'] = df.loc[test_idx, '1step_test24c'].replace(np.nan, last_forecast_test_24c)
last_forecast_train_24c = prev[train_idx][-2] + Ptrain24c[-1]
df.loc[train_idx, '1step_test24c'] = df.loc[train_idx, '1step_train24c'].replace(np.nan, last_forecast_train_24c)
last_forecast_test_25c = prev[test_idx][-2] + Ptest25c[-1]
df.loc[test_idx, '1step_test25c'] = df.loc[test_idx, '1step_test25c'].replace(np.nan, last_forecast_test_25c)
last_forecast_train_25c = prev[train_idx][-2] + Ptrain25c[-1]
df.loc[train_idx, '1step_test25c'] = df.loc[train_idx, '1step_train25c'].replace(np.nan, last_forecast_train_25c)
last_forecast_test_26c = prev[test_idx][-2] + Ptest26c[-1]
df.loc[test_idx, '1step_test26c'] = df.loc[test_idx, '1step_test26c'].replace(np.nan, last_forecast_test_26c)
last_forecast_train_26c = prev[train_idx][-2] + Ptrain26c[-1]
df.loc[train_idx, '1step_test26c'] = df.loc[train_idx, '1step_train26c'].replace(np.nan, last_forecast_train_26c)
last_forecast_test_27c = prev[test_idx][-2] + Ptest27c[-1]
df.loc[test_idx, '1step_test27c'] = df.loc[test_idx, '1step_test27c'].replace(np.nan, last_forecast_test_27c)
last_forecast_train_27c = prev[train_idx][-2] + Ptrain27c[-1]
df.loc[train_idx, '1step_test27c'] = df.loc[train_idx, '1step_train27c'].replace(np.nan, last_forecast_train_27c)
last_forecast_test_28c = prev[test_idx][-2] + Ptest28c[-1]
df.loc[test_idx, '1step_test28c'] = df.loc[test_idx, '1step_test28c'].replace(np.nan, last_forecast_test_28c)
last_forecast_train_28c = prev[train_idx][-2] + Ptrain28c[-1]
df.loc[train_idx, '1step_test28c'] = df.loc[train_idx, '1step_train28c'].replace(np.nan, last_forecast_train_28c)
last_forecast_test_29c = prev[test_idx][-2] + Ptest29c[-1]
df.loc[test_idx, '1step_test29c'] = df.loc[test_idx, '1step_test29c'].replace(np.nan, last_forecast_test_29c)
last_forecast_train_29c = prev[train_idx][-2] + Ptrain29c[-1]
df.loc[train_idx, '1step_test29c'] = df.loc[train_idx, '1step_train29c'].replace(np.nan, last_forecast_train_29c)
last_forecast_test_30c = prev[test_idx][-2] + Ptest30c[-1]
df.loc[test_idx, '1step_test30c'] = df.loc[test_idx, '1step_test30c'].replace(np.nan, last_forecast_test_30c)
last_forecast_train_30c = prev[train_idx][-2] + Ptrain30c[-1]
df.loc[train_idx, '1step_test30c'] = df.loc[train_idx, '1step_train30c'].replace(np.nan, last_forecast_train_30c)
last_forecast_test_31c = prev[test_idx][-2] + Ptest31c[-1]
df.loc[test_idx, '1step_test31c'] = df.loc[test_idx, '1step_test31c'].replace(np.nan, last_forecast_test_31c)
last_forecast_train_31c = prev[train_idx][-2] + Ptrain31c[-1]
df.loc[train_idx, '1step_test31c'] = df.loc[train_idx, '1step_train31c'].replace(np.nan, last_forecast_train_31c)
last_forecast_test_32c = prev[test_idx][-2] + Ptest32c[-1]
df.loc[test_idx, '1step_test32c'] = df.loc[test_idx, '1step_test32c'].replace(np.nan, last_forecast_test_32c)
last_forecast_train_32c = prev[train_idx][-2] + Ptrain32c[-1]
df.loc[train_idx, '1step_test32c'] = df.loc[train_idx, '1step_train32c'].replace(np.nan, last_forecast_train_32c)
last_forecast_test_33c = prev[test_idx][-2] + Ptest33c[-1]
df.loc[test_idx, '1step_test33c'] = df.loc[test_idx, '1step_test33c'].replace(np.nan, last_forecast_test_33c)
last_forecast_train_33c = prev[train_idx][-2] + Ptrain33c[-1]
df.loc[train_idx, '1step_test33c'] = df.loc[train_idx, '1step_train33c'].replace(np.nan, last_forecast_train_33c)
last_forecast_test_34c = prev[test_idx][-2] + Ptest34c[-1]
df.loc[test_idx, '1step_test34c'] = df.loc[test_idx, '1step_test34c'].replace(np.nan, last_forecast_test_34c)
last_forecast_train_34c = prev[train_idx][-2] + Ptrain34c[-1]
df.loc[train_idx, '1step_test34c'] = df.loc[train_idx, '1step_train34c'].replace(np.nan, last_forecast_train_34c)
last_forecast_test_35c = prev[test_idx][-2] + Ptest35c[-1]
df.loc[test_idx, '1step_test35c'] = df.loc[test_idx, '1step_test35c'].replace(np.nan, last_forecast_test_35c)
last_forecast_train_35c = prev[train_idx][-2] + Ptrain35c[-1]
df.loc[train_idx, '1step_test35c'] = df.loc[train_idx, '1step_train35c'].replace(np.nan, last_forecast_train_35c)
last_forecast_test_36c = prev[test_idx][-2] + Ptest36c[-1]
df.loc[test_idx, '1step_test36c'] = df.loc[test_idx, '1step_test36c'].replace(np.nan, last_forecast_test_36c)
last_forecast_train_36c = prev[train_idx][-2] + Ptrain36c[-1]
df.loc[train_idx, '1step_test36c'] = df.loc[train_idx, '1step_train36c'].replace(np.nan, last_forecast_train_36c)
#Al regresar los datos se pierde un dato al final, se añade aquí: LSTM 2 CAPAS
last_forecast_test_2d = prev[test_idx][-2] + Ptest2d[-1]
df.loc[test_idx, '1step_test2d'] = df.loc[test_idx, '1step_test2d'].replace(np.nan, last_forecast_test_2d)
last_forecast_train_2d = prev[train_idx][-2] + Ptrain2d[-1]
df.loc[train_idx, '1step_test2d'] = df.loc[train_idx, '1step_train2d'].replace(np.nan, last_forecast_train_2d)
last_forecast_test_3d = prev[test_idx][-2] + Ptest3d[-1]
df.loc[test_idx, '1step_test3d'] = df.loc[test_idx, '1step_test3d'].replace(np.nan, last_forecast_test_3d)
last_forecast_train_3d= prev[train_idx][-2] + Ptrain3d[-1]
df.loc[train_idx, '1step_test3d'] = df.loc[train_idx, '1step_train3d'].replace(np.nan, last_forecast_train_3d)
last_forecast_test_4d = prev[test_idx][-2] + Ptest4d[-1]
df.loc[test_idx, '1step_test4d'] = df.loc[test_idx, '1step_test4d'].replace(np.nan, last_forecast_test_4d)
last_forecast_train_4d = prev[train_idx][-2] + Ptrain4d[-1]
df.loc[train_idx, '1step_test4d'] = df.loc[train_idx, '1step_train4d'].replace(np.nan, last_forecast_train_4d)
last_forecast_test_5d = prev[test_idx][-2] + Ptest5d[-1]
df.loc[test_idx, '1step_test5d'] = df.loc[test_idx, '1step_test5d'].replace(np.nan, last_forecast_test_5d)
last_forecast_train_5d = prev[train_idx][-2] + Ptrain5d[-1]
df.loc[train_idx, '1step_test5d'] = df.loc[train_idx, '1step_train5d'].replace(np.nan, last_forecast_train_5d)
last_forecast_test_6d = prev[test_idx][-2] + Ptest6d[-1]
df.loc[test_idx, '1step_test6d'] = df.loc[test_idx, '1step_test6d'].replace(np.nan, last_forecast_test_6d)
last_forecast_train_6d = prev[train_idx][-2] + Ptrain6d[-1]
df.loc[train_idx, '1step_test6d'] = df.loc[train_idx, '1step_train6d'].replace(np.nan, last_forecast_train_6d)
last_forecast_test_7d = prev[test_idx][-2] + Ptest7d[-1]
df.loc[test_idx, '1step_test7d'] = df.loc[test_idx, '1step_test7d'].replace(np.nan, last_forecast_test_7d)
last_forecast_train_7d = prev[train_idx][-2] + Ptrain7d[-1]
df.loc[train_idx, '1step_test7d'] = df.loc[train_idx, '1step_train7d'].replace(np.nan, last_forecast_train_7d)
last_forecast_test_8d = prev[test_idx][-2] + Ptest8d[-1]
df.loc[test_idx, '1step_test8d'] = df.loc[test_idx, '1step_test8d'].replace(np.nan, last_forecast_test_8d)
last_forecast_train_8d = prev[train_idx][-2] + Ptrain8d[-1]
df.loc[train_idx, '1step_test8d'] = df.loc[train_idx, '1step_train8d'].replace(np.nan, last_forecast_train_8d)
last_forecast_test_9d = prev[test_idx][-2] + Ptest9d[-1]
df.loc[test_idx, '1step_test9d'] = df.loc[test_idx, '1step_test9d'].replace(np.nan, last_forecast_test_9d)
last_forecast_train_9d = prev[train_idx][-2] + Ptrain9d[-1]
df.loc[train_idx, '1step_test9d'] = df.loc[train_idx, '1step_train9d'].replace(np.nan, last_forecast_train_9d)
last_forecast_test_10d = prev[test_idx][-2] + Ptest10d[-1]
df.loc[test_idx, '1step_test10d'] = df.loc[test_idx, '1step_test10d'].replace(np.nan, last_forecast_test_10d)
last_forecast_train_10d = prev[train_idx][-2] + Ptrain10d[-1]
df.loc[train_idx, '1step_test10d'] = df.loc[train_idx, '1step_train10d'].replace(np.nan, last_forecast_train_10d)
last_forecast_test_11d = prev[test_idx][-2] + Ptest11d[-1]
df.loc[test_idx, '1step_test11d'] = df.loc[test_idx, '1step_test11d'].replace(np.nan, last_forecast_test_11d)
last_forecast_train_11d = prev[train_idx][-2] + Ptrain11d[-1]
df.loc[train_idx, '1step_test11d'] = df.loc[train_idx, '1step_train11d'].replace(np.nan, last_forecast_train_11d)
last_forecast_test_12d = prev[test_idx][-2] + Ptest12d[-1]
df.loc[test_idx, '1step_test12d'] = df.loc[test_idx, '1step_test12d'].replace(np.nan, last_forecast_test_12d)
last_forecast_train_12d = prev[train_idx][-2] + Ptrain12d[-1]
df.loc[train_idx, '1step_test12d'] = df.loc[train_idx, '1step_train12d'].replace(np.nan, last_forecast_train_12d)
last_forecast_test_13d = prev[test_idx][-2] + Ptest13d[-1]
df.loc[test_idx, '1step_test13d'] = df.loc[test_idx, '1step_test13d'].replace(np.nan, last_forecast_test_13d)
last_forecast_train_13d = prev[train_idx][-2] + Ptrain13d[-1]
df.loc[train_idx, '1step_test13d'] = df.loc[train_idx, '1step_train13d'].replace(np.nan, last_forecast_train_13d)
last_forecast_test_14d = prev[test_idx][-2] + Ptest14d[-1]
df.loc[test_idx, '1step_test14d'] = df.loc[test_idx, '1step_test14d'].replace(np.nan, last_forecast_test_14d)
last_forecast_train_14d = prev[train_idx][-2] + Ptrain14d[-1]
df.loc[train_idx, '1step_test14d'] = df.loc[train_idx, '1step_train14d'].replace(np.nan, last_forecast_train_14d)
last_forecast_test_15d = prev[test_idx][-2] + Ptest15d[-1]
df.loc[test_idx, '1step_test15d'] = df.loc[test_idx, '1step_test15d'].replace(np.nan, last_forecast_test_15d)
last_forecast_train_15d = prev[train_idx][-2] + Ptrain15d[-1]
df.loc[train_idx, '1step_test15d'] = df.loc[train_idx, '1step_train15d'].replace(np.nan, last_forecast_train_15d)
last_forecast_test_16d = prev[test_idx][-2] + Ptest16d[-1]
df.loc[test_idx, '1step_test16d'] = df.loc[test_idx, '1step_test16d'].replace(np.nan, last_forecast_test_16d)
last_forecast_train_16d= prev[train_idx][-2] + Ptrain16d[-1]
df.loc[train_idx, '1step_test16d'] = df.loc[train_idx, '1step_train16d'].replace(np.nan, last_forecast_train_16d)
last_forecast_test_17d = prev[test_idx][-2] + Ptest17d[-1]
df.loc[test_idx, '1step_test17d'] = df.loc[test_idx, '1step_test17d'].replace(np.nan, last_forecast_test_17d)
last_forecast_train_17d = prev[train_idx][-2] + Ptrain17d[-1]
df.loc[train_idx, '1step_test17d'] = df.loc[train_idx, '1step_train17d'].replace(np.nan, last_forecast_train_17d)
last_forecast_test_18d = prev[test_idx][-2] + Ptest18d[-1]
df.loc[test_idx, '1step_test18d'] = df.loc[test_idx, '1step_test18d'].replace(np.nan, last_forecast_test_18d)
last_forecast_train_18d = prev[train_idx][-2] + Ptrain18d[-1]
df.loc[train_idx, '1step_test18d'] = df.loc[train_idx, '1step_train18d'].replace(np.nan, last_forecast_train_18d)
last_forecast_test_19d = prev[test_idx][-2] + Ptest19d[-1]
df.loc[test_idx, '1step_test19d'] = df.loc[test_idx, '1step_test19d'].replace(np.nan, last_forecast_test_19d)
last_forecast_train_19d = prev[train_idx][-2] + Ptrain19d[-1]
df.loc[train_idx, '1step_test19d'] = df.loc[train_idx, '1step_train19d'].replace(np.nan, last_forecast_train_19d)
last_forecast_test_20d = prev[test_idx][-2] + Ptest20d[-1]
df.loc[test_idx, '1step_test20d'] = df.loc[test_idx, '1step_test20d'].replace(np.nan, last_forecast_test_20d)
last_forecast_train_20d = prev[train_idx][-2] + Ptrain20d[-1]
df.loc[train_idx, '1step_test20d'] = df.loc[train_idx, '1step_train20d'].replace(np.nan, last_forecast_train_20d)
last_forecast_test_21d = prev[test_idx][-2] + Ptest21d[-1]
df.loc[test_idx, '1step_test21d'] = df.loc[test_idx, '1step_test21d'].replace(np.nan, last_forecast_test_21d)
last_forecast_train_21d = prev[train_idx][-2] + Ptrain21d[-1]
df.loc[train_idx, '1step_test21d'] = df.loc[train_idx, '1step_train21d'].replace(np.nan, last_forecast_train_21d)
last_forecast_test_22d = prev[test_idx][-2] + Ptest22d[-1]
df.loc[test_idx, '1step_test22d'] = df.loc[test_idx, '1step_test22d'].replace(np.nan, last_forecast_test_22d)
last_forecast_train_22d = prev[train_idx][-2] + Ptrain22d[-1]
df.loc[train_idx, '1step_test22d'] = df.loc[train_idx, '1step_train22d'].replace(np.nan, last_forecast_train_22d)
last_forecast_test_23d = prev[test_idx][-2] + Ptest23d[-1]
df.loc[test_idx, '1step_test23d'] = df.loc[test_idx, '1step_test23d'].replace(np.nan, last_forecast_test_23d)
last_forecast_train_23d = prev[train_idx][-2] + Ptrain23d[-1]
df.loc[train_idx, '1step_test23d'] = df.loc[train_idx, '1step_train23d'].replace(np.nan, last_forecast_train_23d)
last_forecast_test_24d = prev[test_idx][-2] + Ptest24d[-1]
df.loc[test_idx, '1step_test24d'] = df.loc[test_idx, '1step_test24d'].replace(np.nan, last_forecast_test_24d)
last_forecast_train_24d = prev[train_idx][-2] + Ptrain24d[-1]
df.loc[train_idx, '1step_test24d'] = df.loc[train_idx, '1step_train24d'].replace(np.nan, last_forecast_train_24d)
last_forecast_test_25d = prev[test_idx][-2] + Ptest25d[-1]
df.loc[test_idx, '1step_test25d'] = df.loc[test_idx, '1step_test25d'].replace(np.nan, last_forecast_test_25d)
last_forecast_train_25d = prev[train_idx][-2] + Ptrain25d[-1]
df.loc[train_idx, '1step_test25d'] = df.loc[train_idx, '1step_train25d'].replace(np.nan, last_forecast_train_25d)
last_forecast_test_26d = prev[test_idx][-2] + Ptest26d[-1]
df.loc[test_idx, '1step_test26d'] = df.loc[test_idx, '1step_test26d'].replace(np.nan, last_forecast_test_26d)
last_forecast_train_26d = prev[train_idx][-2] + Ptrain26d[-1]
df.loc[train_idx, '1step_test26d'] = df.loc[train_idx, '1step_train26d'].replace(np.nan, last_forecast_train_26d)
last_forecast_test_27d = prev[test_idx][-2] + Ptest27d[-1]
df.loc[test_idx, '1step_test27d'] = df.loc[test_idx, '1step_test27d'].replace(np.nan, last_forecast_test_27d)
last_forecast_train_27d = prev[train_idx][-2] + Ptrain27d[-1]
df.loc[train_idx, '1step_test27d'] = df.loc[train_idx, '1step_train27d'].replace(np.nan, last_forecast_train_27d)
last_forecast_test_28d = prev[test_idx][-2] + Ptest28d[-1]
df.loc[test_idx, '1step_test28d'] = df.loc[test_idx, '1step_test28d'].replace(np.nan, last_forecast_test_28d)
last_forecast_train_28d = prev[train_idx][-2] + Ptrain28d[-1]
df.loc[train_idx, '1step_test28d'] = df.loc[train_idx, '1step_train28d'].replace(np.nan, last_forecast_train_28d)
last_forecast_test_29d = prev[test_idx][-2] + Ptest29d[-1]
df.loc[test_idx, '1step_test29d'] = df.loc[test_idx, '1step_test29d'].replace(np.nan, last_forecast_test_29d)
last_forecast_train_29d = prev[train_idx][-2] + Ptrain29d[-1]
df.loc[train_idx, '1step_test29d'] = df.loc[train_idx, '1step_train29d'].replace(np.nan, last_forecast_train_29d)
last_forecast_test_30d = prev[test_idx][-2] + Ptest30d[-1]
df.loc[test_idx, '1step_test30d'] = df.loc[test_idx, '1step_test30d'].replace(np.nan, last_forecast_test_30d)
last_forecast_train_30d = prev[train_idx][-2] + Ptrain30d[-1]
df.loc[train_idx, '1step_test30d'] = df.loc[train_idx, '1step_train30d'].replace(np.nan, last_forecast_train_30d)
last_forecast_test_31d = prev[test_idx][-2] + Ptest31d[-1]
df.loc[test_idx, '1step_test31d'] = df.loc[test_idx, '1step_test31d'].replace(np.nan, last_forecast_test_31d)
last_forecast_train_31d = prev[train_idx][-2] + Ptrain31d[-1]
df.loc[train_idx, '1step_test31d'] = df.loc[train_idx, '1step_train31d'].replace(np.nan, last_forecast_train_31d)
last_forecast_test_32d = prev[test_idx][-2] + Ptest32d[-1]
df.loc[test_idx, '1step_test32d'] = df.loc[test_idx, '1step_test32d'].replace(np.nan, last_forecast_test_32d)
last_forecast_train_32d = prev[train_idx][-2] + Ptrain32d[-1]
df.loc[train_idx, '1step_test32d'] = df.loc[train_idx, '1step_train32d'].replace(np.nan, last_forecast_train_32d)
last_forecast_test_33d = prev[test_idx][-2] + Ptest33d[-1]
df.loc[test_idx, '1step_test33d'] = df.loc[test_idx, '1step_test33d'].replace(np.nan, last_forecast_test_33d)
last_forecast_train_33d = prev[train_idx][-2] + Ptrain33d[-1]
df.loc[train_idx, '1step_test33d'] = df.loc[train_idx, '1step_train33d'].replace(np.nan, last_forecast_train_33d)
last_forecast_test_34d = prev[test_idx][-2] + Ptest34d[-1]
df.loc[test_idx, '1step_test34d'] = df.loc[test_idx, '1step_test34d'].replace(np.nan, last_forecast_test_34d)
last_forecast_train_34d = prev[train_idx][-2] + Ptrain34d[-1]
df.loc[train_idx, '1step_test34d'] = df.loc[train_idx, '1step_train34d'].replace(np.nan, last_forecast_train_34d)
last_forecast_test_35d = prev[test_idx][-2] + Ptest35d[-1]
df.loc[test_idx, '1step_test35d'] = df.loc[test_idx, '1step_test35d'].replace(np.nan, last_forecast_test_35d)
last_forecast_train_35d = prev[train_idx][-2] + Ptrain35d[-1]
df.loc[train_idx, '1step_test35d'] = df.loc[train_idx, '1step_train35d'].replace(np.nan, last_forecast_train_35d)
last_forecast_test_36d = prev[test_idx][-2] + Ptest36d[-1]
df.loc[test_idx, '1step_test36d'] = df.loc[test_idx, '1step_test36d'].replace(np.nan, last_forecast_test_36d)
last_forecast_train_36d = prev[train_idx][-2] + Ptrain36d[-1]
df.loc[train_idx, '1step_test36d'] = df.loc[train_idx, '1step_train36d'].replace(np.nan, last_forecast_train_36d)
#Al regresar los datos se pierde un dato al final, se añade aquí: BI-LSTM 1 CAPA
last_forecast_test_2e = prev[test_idx][-2] + Ptest2e[-1]
df.loc[test_idx, '1step_test2e'] = df.loc[test_idx, '1step_test2e'].replace(np.nan, last_forecast_test_2e)
last_forecast_train_2e = prev[train_idx][-2] + Ptrain2e[-1]
df.loc[train_idx, '1step_test2e'] = df.loc[train_idx, '1step_train2e'].replace(np.nan, last_forecast_train_2e)
last_forecast_test_3e = prev[test_idx][-2] + Ptest3e[-1]
df.loc[test_idx, '1step_test3e'] = df.loc[test_idx, '1step_test3e'].replace(np.nan, last_forecast_test_3e)
last_forecast_train_3e= prev[train_idx][-2] + Ptrain3e[-1]
df.loc[train_idx, '1step_test3e'] = df.loc[train_idx, '1step_train3e'].replace(np.nan, last_forecast_train_3e)
last_forecast_test_4e = prev[test_idx][-2] + Ptest4e[-1]
df.loc[test_idx, '1step_test4e'] = df.loc[test_idx, '1step_test4e'].replace(np.nan, last_forecast_test_4e)
last_forecast_train_4e = prev[train_idx][-2] + Ptrain4e[-1]
df.loc[train_idx, '1step_test4e'] = df.loc[train_idx, '1step_train4e'].replace(np.nan, last_forecast_train_4e)
last_forecast_test_5e = prev[test_idx][-2] + Ptest5e[-1]
df.loc[test_idx, '1step_test5e'] = df.loc[test_idx, '1step_test5e'].replace(np.nan, last_forecast_test_5e)
last_forecast_train_5e = prev[train_idx][-2] + Ptrain5e[-1]
df.loc[train_idx, '1step_test5e'] = df.loc[train_idx, '1step_train5e'].replace(np.nan, last_forecast_train_5e)
last_forecast_test_6e = prev[test_idx][-2] + Ptest6e[-1]
df.loc[test_idx, '1step_test6e'] = df.loc[test_idx, '1step_test6e'].replace(np.nan, last_forecast_test_6e)
last_forecast_train_6e = prev[train_idx][-2] + Ptrain6e[-1]
df.loc[train_idx, '1step_test6e'] = df.loc[train_idx, '1step_train6e'].replace(np.nan, last_forecast_train_6e)
last_forecast_test_7e = prev[test_idx][-2] + Ptest7e[-1]
df.loc[test_idx, '1step_test7e'] = df.loc[test_idx, '1step_test7e'].replace(np.nan, last_forecast_test_7e)
last_forecast_train_7e = prev[train_idx][-2] + Ptrain7e[-1]
df.loc[train_idx, '1step_test7e'] = df.loc[train_idx, '1step_train7e'].replace(np.nan, last_forecast_train_7e)
last_forecast_test_8e = prev[test_idx][-2] + Ptest8e[-1]
df.loc[test_idx, '1step_test8e'] = df.loc[test_idx, '1step_test8e'].replace(np.nan, last_forecast_test_8e)
last_forecast_train_8e = prev[train_idx][-2] + Ptrain8e[-1]
df.loc[train_idx, '1step_test8e'] = df.loc[train_idx, '1step_train8e'].replace(np.nan, last_forecast_train_8e)
last_forecast_test_9e = prev[test_idx][-2] + Ptest9e[-1]
df.loc[test_idx, '1step_test9e'] = df.loc[test_idx, '1step_test9e'].replace(np.nan, last_forecast_test_9e)
last_forecast_train_9e = prev[train_idx][-2] + Ptrain9e[-1]
df.loc[train_idx, '1step_test9e'] = df.loc[train_idx, '1step_train9e'].replace(np.nan, last_forecast_train_9e)
last_forecast_test_10e = prev[test_idx][-2] + Ptest10e[-1]
df.loc[test_idx, '1step_test10e'] = df.loc[test_idx, '1step_test10e'].replace(np.nan, last_forecast_test_10e)
last_forecast_train_10e = prev[train_idx][-2] + Ptrain10e[-1]
df.loc[train_idx, '1step_test10e'] = df.loc[train_idx, '1step_train10e'].replace(np.nan, last_forecast_train_10e)
last_forecast_test_11e = prev[test_idx][-2] + Ptest11e[-1]
df.loc[test_idx, '1step_test11e'] = df.loc[test_idx, '1step_test11e'].replace(np.nan, last_forecast_test_11e)
last_forecast_train_11e = prev[train_idx][-2] + Ptrain11e[-1]
df.loc[train_idx, '1step_test11e'] = df.loc[train_idx, '1step_train11e'].replace(np.nan, last_forecast_train_11e)
last_forecast_test_12e = prev[test_idx][-2] + Ptest12e[-1]
df.loc[test_idx, '1step_test12e'] = df.loc[test_idx, '1step_test12e'].replace(np.nan, last_forecast_test_12e)
last_forecast_train_12e = prev[train_idx][-2] + Ptrain12e[-1]
df.loc[train_idx, '1step_test12e'] = df.loc[train_idx, '1step_train12e'].replace(np.nan, last_forecast_train_12e)
last_forecast_test_13e = prev[test_idx][-2] + Ptest13e[-1]
df.loc[test_idx, '1step_test13e'] = df.loc[test_idx, '1step_test13e'].replace(np.nan, last_forecast_test_13e)
last_forecast_train_13e = prev[train_idx][-2] + Ptrain13e[-1]
df.loc[train_idx, '1step_test13e'] = df.loc[train_idx, '1step_train13e'].replace(np.nan, last_forecast_train_13e)
last_forecast_test_14e = prev[test_idx][-2] + Ptest14e[-1]
df.loc[test_idx, '1step_test14e'] = df.loc[test_idx, '1step_test14e'].replace(np.nan, last_forecast_test_14e)
last_forecast_train_14e = prev[train_idx][-2] + Ptrain14e[-1]
df.loc[train_idx, '1step_test14e'] = df.loc[train_idx, '1step_train14e'].replace(np.nan, last_forecast_train_14e)
last_forecast_test_15e = prev[test_idx][-2] + Ptest15e[-1]
df.loc[test_idx, '1step_test15e'] = df.loc[test_idx, '1step_test15e'].replace(np.nan, last_forecast_test_15e)
last_forecast_train_15e = prev[train_idx][-2] + Ptrain15e[-1]
df.loc[train_idx, '1step_test15e'] = df.loc[train_idx, '1step_train15e'].replace(np.nan, last_forecast_train_15e)
last_forecast_test_16e = prev[test_idx][-2] + Ptest16e[-1]
df.loc[test_idx, '1step_test16e'] = df.loc[test_idx, '1step_test16e'].replace(np.nan, last_forecast_test_16e)
last_forecast_train_16e= prev[train_idx][-2] + Ptrain16e[-1]
df.loc[train_idx, '1step_test16e'] = df.loc[train_idx, '1step_train16e'].replace(np.nan, last_forecast_train_16e)
last_forecast_test_17e = prev[test_idx][-2] + Ptest17e[-1]
df.loc[test_idx, '1step_test17e'] = df.loc[test_idx, '1step_test17e'].replace(np.nan, last_forecast_test_17e)
last_forecast_train_17e = prev[train_idx][-2] + Ptrain17e[-1]
df.loc[train_idx, '1step_test17e'] = df.loc[train_idx, '1step_train17e'].replace(np.nan, last_forecast_train_17e)
last_forecast_test_18e = prev[test_idx][-2] + Ptest18e[-1]
df.loc[test_idx, '1step_test18e'] = df.loc[test_idx, '1step_test18e'].replace(np.nan, last_forecast_test_18e)
last_forecast_train_18e = prev[train_idx][-2] + Ptrain18e[-1]
df.loc[train_idx, '1step_test18e'] = df.loc[train_idx, '1step_train18e'].replace(np.nan, last_forecast_train_18e)
last_forecast_test_19e = prev[test_idx][-2] + Ptest19e[-1]
df.loc[test_idx, '1step_test19e'] = df.loc[test_idx, '1step_test19e'].replace(np.nan, last_forecast_test_19e)
last_forecast_train_19e = prev[train_idx][-2] + Ptrain19e[-1]
df.loc[train_idx, '1step_test19e'] = df.loc[train_idx, '1step_train19e'].replace(np.nan, last_forecast_train_19e)
last_forecast_test_20e = prev[test_idx][-2] + Ptest20e[-1]
df.loc[test_idx, '1step_test20e'] = df.loc[test_idx, '1step_test20e'].replace(np.nan, last_forecast_test_20e)
last_forecast_train_20e = prev[train_idx][-2] + Ptrain20e[-1]
df.loc[train_idx, '1step_test20e'] = df.loc[train_idx, '1step_train20e'].replace(np.nan, last_forecast_train_20e)
last_forecast_test_21e = prev[test_idx][-2] + Ptest21e[-1]
df.loc[test_idx, '1step_test21e'] = df.loc[test_idx, '1step_test21e'].replace(np.nan, last_forecast_test_21e)
last_forecast_train_21e = prev[train_idx][-2] + Ptrain21e[-1]
df.loc[train_idx, '1step_test21e'] = df.loc[train_idx, '1step_train21e'].replace(np.nan, last_forecast_train_21e)
last_forecast_test_22e = prev[test_idx][-2] + Ptest22e[-1]
df.loc[test_idx, '1step_test22e'] = df.loc[test_idx, '1step_test22e'].replace(np.nan, last_forecast_test_22e)
last_forecast_train_22e = prev[train_idx][-2] + Ptrain22e[-1]
df.loc[train_idx, '1step_test22e'] = df.loc[train_idx, '1step_train22e'].replace(np.nan, last_forecast_train_22e)
last_forecast_test_23e = prev[test_idx][-2] + Ptest23e[-1]
df.loc[test_idx, '1step_test23e'] = df.loc[test_idx, '1step_test23e'].replace(np.nan, last_forecast_test_23e)
last_forecast_train_23e = prev[train_idx][-2] + Ptrain23e[-1]
df.loc[train_idx, '1step_test23e'] = df.loc[train_idx, '1step_train23e'].replace(np.nan, last_forecast_train_23e)
last_forecast_test_24e = prev[test_idx][-2] + Ptest24e[-1]
df.loc[test_idx, '1step_test24e'] = df.loc[test_idx, '1step_test24e'].replace(np.nan, last_forecast_test_24e)
last_forecast_train_24e = prev[train_idx][-2] + Ptrain24e[-1]
df.loc[train_idx, '1step_test24e'] = df.loc[train_idx, '1step_train24e'].replace(np.nan, last_forecast_train_24e)
last_forecast_test_25e = prev[test_idx][-2] + Ptest25e[-1]
df.loc[test_idx, '1step_test25e'] = df.loc[test_idx, '1step_test25e'].replace(np.nan, last_forecast_test_25e)
last_forecast_train_25e = prev[train_idx][-2] + Ptrain25e[-1]
df.loc[train_idx, '1step_test25e'] = df.loc[train_idx, '1step_train25e'].replace(np.nan, last_forecast_train_25e)
last_forecast_test_26e = prev[test_idx][-2] + Ptest26e[-1]
df.loc[test_idx, '1step_test26e'] = df.loc[test_idx, '1step_test26e'].replace(np.nan, last_forecast_test_26e)
last_forecast_train_26e = prev[train_idx][-2] + Ptrain26e[-1]
df.loc[train_idx, '1step_test26e'] = df.loc[train_idx, '1step_train26e'].replace(np.nan, last_forecast_train_26e)
last_forecast_test_27e = prev[test_idx][-2] + Ptest27e[-1]
df.loc[test_idx, '1step_test27e'] = df.loc[test_idx, '1step_test27e'].replace(np.nan, last_forecast_test_27e)
last_forecast_train_27e = prev[train_idx][-2] + Ptrain27e[-1]
df.loc[train_idx, '1step_test27e'] = df.loc[train_idx, '1step_train27e'].replace(np.nan, last_forecast_train_27e)
last_forecast_test_28e = prev[test_idx][-2] + Ptest28e[-1]
df.loc[test_idx, '1step_test28e'] = df.loc[test_idx, '1step_test28e'].replace(np.nan, last_forecast_test_28e)
last_forecast_train_28e = prev[train_idx][-2] + Ptrain28e[-1]
df.loc[train_idx, '1step_test28e'] = df.loc[train_idx, '1step_train28e'].replace(np.nan, last_forecast_train_28e)
last_forecast_test_29e = prev[test_idx][-2] + Ptest29e[-1]
df.loc[test_idx, '1step_test29e'] = df.loc[test_idx, '1step_test29e'].replace(np.nan, last_forecast_test_29e)
last_forecast_train_29e = prev[train_idx][-2] + Ptrain29e[-1]
df.loc[train_idx, '1step_test29e'] = df.loc[train_idx, '1step_train29e'].replace(np.nan, last_forecast_train_29e)
last_forecast_test_30e = prev[test_idx][-2] + Ptest30e[-1]
df.loc[test_idx, '1step_test30e'] = df.loc[test_idx, '1step_test30e'].replace(np.nan, last_forecast_test_30e)
last_forecast_train_30e = prev[train_idx][-2] + Ptrain30e[-1]
df.loc[train_idx, '1step_test30e'] = df.loc[train_idx, '1step_train30e'].replace(np.nan, last_forecast_train_30e)
last_forecast_test_31e = prev[test_idx][-2] + Ptest31e[-1]
df.loc[test_idx, '1step_test31e'] = df.loc[test_idx, '1step_test31e'].replace(np.nan, last_forecast_test_31e)
last_forecast_train_31e = prev[train_idx][-2] + Ptrain31e[-1]
df.loc[train_idx, '1step_test31e'] = df.loc[train_idx, '1step_train31e'].replace(np.nan, last_forecast_train_31e)
last_forecast_test_32e = prev[test_idx][-2] + Ptest32e[-1]
df.loc[test_idx, '1step_test32e'] = df.loc[test_idx, '1step_test32e'].replace(np.nan, last_forecast_test_32e)
last_forecast_train_32e = prev[train_idx][-2] + Ptrain32e[-1]
df.loc[train_idx, '1step_test32e'] = df.loc[train_idx, '1step_train32e'].replace(np.nan, last_forecast_train_32e)
last_forecast_test_33e = prev[test_idx][-2] + Ptest33e[-1]
df.loc[test_idx, '1step_test33e'] = df.loc[test_idx, '1step_test33e'].replace(np.nan, last_forecast_test_33e)
last_forecast_train_33e = prev[train_idx][-2] + Ptrain33e[-1]
df.loc[train_idx, '1step_test33e'] = df.loc[train_idx, '1step_train33e'].replace(np.nan, last_forecast_train_33e)
last_forecast_test_34e = prev[test_idx][-2] + Ptest34e[-1]
df.loc[test_idx, '1step_test34e'] = df.loc[test_idx, '1step_test34e'].replace(np.nan, last_forecast_test_34e)
last_forecast_train_34e = prev[train_idx][-2] + Ptrain34e[-1]
df.loc[train_idx, '1step_test34e'] = df.loc[train_idx, '1step_train34e'].replace(np.nan, last_forecast_train_34e)
last_forecast_test_35e = prev[test_idx][-2] + Ptest35e[-1]
df.loc[test_idx, '1step_test35e'] = df.loc[test_idx, '1step_test35e'].replace(np.nan, last_forecast_test_35e)
last_forecast_train_35e = prev[train_idx][-2] + Ptrain35e[-1]
df.loc[train_idx, '1step_test35e'] = df.loc[train_idx, '1step_train35e'].replace(np.nan, last_forecast_train_35e)
last_forecast_test_36e = prev[test_idx][-2] + Ptest36e[-1]
df.loc[test_idx, '1step_test36e'] = df.loc[test_idx, '1step_test36e'].replace(np.nan, last_forecast_test_36e)
last_forecast_train_36e = prev[train_idx][-2] + Ptrain36e[-1]
df.loc[train_idx, '1step_test36e'] = df.loc[train_idx, '1step_train36e'].replace(np.nan, last_forecast_train_36e)
#Al regresar los datos se pierde un dato al final, se añade aquí: BI-LSTM 2 CAPAS
last_forecast_test_2f = prev[test_idx][-2] + Ptest2f[-1]
df.loc[test_idx, '1step_test2f'] = df.loc[test_idx, '1step_test2f'].replace(np.nan, last_forecast_test_2f)
last_forecast_train_2f = prev[train_idx][-2] + Ptrain2f[-1]
df.loc[train_idx, '1step_test2f'] = df.loc[train_idx, '1step_train2f'].replace(np.nan, last_forecast_train_2f)
last_forecast_test_3f = prev[test_idx][-2] + Ptest3f[-1]
df.loc[test_idx, '1step_test3f'] = df.loc[test_idx, '1step_test3f'].replace(np.nan, last_forecast_test_3f)
last_forecast_train_3f= prev[train_idx][-2] + Ptrain3f[-1]
df.loc[train_idx, '1step_test3f'] = df.loc[train_idx, '1step_train3f'].replace(np.nan, last_forecast_train_3f)
last_forecast_test_4f = prev[test_idx][-2] + Ptest4f[-1]
df.loc[test_idx, '1step_test4f'] = df.loc[test_idx, '1step_test4f'].replace(np.nan, last_forecast_test_4f)
last_forecast_train_4f = prev[train_idx][-2] + Ptrain4f[-1]
df.loc[train_idx, '1step_test4f'] = df.loc[train_idx, '1step_train4f'].replace(np.nan, last_forecast_train_4f)
last_forecast_test_5f = prev[test_idx][-2] + Ptest5f[-1]
df.loc[test_idx, '1step_test5f'] = df.loc[test_idx, '1step_test5f'].replace(np.nan, last_forecast_test_5f)
last_forecast_train_5f = prev[train_idx][-2] + Ptrain5f[-1]
df.loc[train_idx, '1step_test5f'] = df.loc[train_idx, '1step_train5f'].replace(np.nan, last_forecast_train_5f)
last_forecast_test_6f = prev[test_idx][-2] + Ptest6f[-1]
df.loc[test_idx, '1step_test6f'] = df.loc[test_idx, '1step_test6f'].replace(np.nan, last_forecast_test_6f)
last_forecast_train_6f = prev[train_idx][-2] + Ptrain6f[-1]
df.loc[train_idx, '1step_test6f'] = df.loc[train_idx, '1step_train6f'].replace(np.nan, last_forecast_train_6f)
last_forecast_test_7f = prev[test_idx][-2] + Ptest7f[-1]
df.loc[test_idx, '1step_test7f'] = df.loc[test_idx, '1step_test7f'].replace(np.nan, last_forecast_test_7f)
last_forecast_train_7f = prev[train_idx][-2] + Ptrain7f[-1]
df.loc[train_idx, '1step_test7f'] = df.loc[train_idx, '1step_train7f'].replace(np.nan, last_forecast_train_7f)
last_forecast_test_8f = prev[test_idx][-2] + Ptest8f[-1]
df.loc[test_idx, '1step_test8f'] = df.loc[test_idx, '1step_test8f'].replace(np.nan, last_forecast_test_8f)
last_forecast_train_8f = prev[train_idx][-2] + Ptrain8f[-1]
df.loc[train_idx, '1step_test8f'] = df.loc[train_idx, '1step_train8f'].replace(np.nan, last_forecast_train_8f)
last_forecast_test_9f = prev[test_idx][-2] + Ptest9f[-1]
df.loc[test_idx, '1step_test9f'] = df.loc[test_idx, '1step_test9f'].replace(np.nan, last_forecast_test_9f)
last_forecast_train_9f = prev[train_idx][-2] + Ptrain9f[-1]
df.loc[train_idx, '1step_test9f'] = df.loc[train_idx, '1step_train9f'].replace(np.nan, last_forecast_train_9f)
last_forecast_test_10f = prev[test_idx][-2] + Ptest10f[-1]
df.loc[test_idx, '1step_test10f'] = df.loc[test_idx, '1step_test10f'].replace(np.nan, last_forecast_test_10f)
last_forecast_train_10f = prev[train_idx][-2] + Ptrain10f[-1]
df.loc[train_idx, '1step_test10f'] = df.loc[train_idx, '1step_train10f'].replace(np.nan, last_forecast_train_10f)
last_forecast_test_11f = prev[test_idx][-2] + Ptest11f[-1]
df.loc[test_idx, '1step_test11f'] = df.loc[test_idx, '1step_test11f'].replace(np.nan, last_forecast_test_11f)
last_forecast_train_11f = prev[train_idx][-2] + Ptrain11f[-1]
df.loc[train_idx, '1step_test11f'] = df.loc[train_idx, '1step_train11f'].replace(np.nan, last_forecast_train_11f)
last_forecast_test_12f = prev[test_idx][-2] + Ptest12f[-1]
df.loc[test_idx, '1step_test12f'] = df.loc[test_idx, '1step_test12f'].replace(np.nan, last_forecast_test_12f)
last_forecast_train_12f = prev[train_idx][-2] + Ptrain12f[-1]
df.loc[train_idx, '1step_test12f'] = df.loc[train_idx, '1step_train12f'].replace(np.nan, last_forecast_train_12f)
last_forecast_test_13f = prev[test_idx][-2] + Ptest13f[-1]
df.loc[test_idx, '1step_test13f'] = df.loc[test_idx, '1step_test13f'].replace(np.nan, last_forecast_test_13f)
last_forecast_train_13f = prev[train_idx][-2] + Ptrain13f[-1]
df.loc[train_idx, '1step_test13f'] = df.loc[train_idx, '1step_train13f'].replace(np.nan, last_forecast_train_13f)
last_forecast_test_14f = prev[test_idx][-2] + Ptest14f[-1]
df.loc[test_idx, '1step_test14f'] = df.loc[test_idx, '1step_test14f'].replace(np.nan, last_forecast_test_14f)
last_forecast_train_14f = prev[train_idx][-2] + Ptrain14f[-1]
df.loc[train_idx, '1step_test14f'] = df.loc[train_idx, '1step_train14f'].replace(np.nan, last_forecast_train_14f)
last_forecast_test_15f = prev[test_idx][-2] + Ptest15f[-1]
df.loc[test_idx, '1step_test15f'] = df.loc[test_idx, '1step_test15f'].replace(np.nan, last_forecast_test_15f)
last_forecast_train_15f = prev[train_idx][-2] + Ptrain15f[-1]
df.loc[train_idx, '1step_test15f'] = df.loc[train_idx, '1step_train15f'].replace(np.nan, last_forecast_train_15f)
last_forecast_test_16f = prev[test_idx][-2] + Ptest16f[-1]
df.loc[test_idx, '1step_test16f'] = df.loc[test_idx, '1step_test16f'].replace(np.nan, last_forecast_test_16f)
last_forecast_train_16f= prev[train_idx][-2] + Ptrain16f[-1]
df.loc[train_idx, '1step_test16f'] = df.loc[train_idx, '1step_train16f'].replace(np.nan, last_forecast_train_16f)
last_forecast_test_17f = prev[test_idx][-2] + Ptest17f[-1]
df.loc[test_idx, '1step_test17f'] = df.loc[test_idx, '1step_test17f'].replace(np.nan, last_forecast_test_17f)
last_forecast_train_17f = prev[train_idx][-2] + Ptrain17f[-1]
df.loc[train_idx, '1step_test17f'] = df.loc[train_idx, '1step_train17f'].replace(np.nan, last_forecast_train_17f)
last_forecast_test_18f = prev[test_idx][-2] + Ptest18f[-1]
df.loc[test_idx, '1step_test18f'] = df.loc[test_idx, '1step_test18f'].replace(np.nan, last_forecast_test_18f)
last_forecast_train_18f = prev[train_idx][-2] + Ptrain18f[-1]
df.loc[train_idx, '1step_test18f'] = df.loc[train_idx, '1step_train18f'].replace(np.nan, last_forecast_train_18f)
last_forecast_test_19f = prev[test_idx][-2] + Ptest19f[-1]
df.loc[test_idx, '1step_test19f'] = df.loc[test_idx, '1step_test19f'].replace(np.nan, last_forecast_test_19f)
last_forecast_train_19f = prev[train_idx][-2] + Ptrain19f[-1]
df.loc[train_idx, '1step_test19f'] = df.loc[train_idx, '1step_train19f'].replace(np.nan, last_forecast_train_19f)
last_forecast_test_20f = prev[test_idx][-2] + Ptest20f[-1]
df.loc[test_idx, '1step_test20f'] = df.loc[test_idx, '1step_test20f'].replace(np.nan, last_forecast_test_20f)
last_forecast_train_20f = prev[train_idx][-2] + Ptrain20f[-1]
df.loc[train_idx, '1step_test20f'] = df.loc[train_idx, '1step_train20f'].replace(np.nan, last_forecast_train_20f)
last_forecast_test_21f = prev[test_idx][-2] + Ptest21f[-1]
df.loc[test_idx, '1step_test21f'] = df.loc[test_idx, '1step_test21f'].replace(np.nan, last_forecast_test_21f)
last_forecast_train_21f = prev[train_idx][-2] + Ptrain21f[-1]
df.loc[train_idx, '1step_test21f'] = df.loc[train_idx, '1step_train21f'].replace(np.nan, last_forecast_train_21f)
last_forecast_test_22f = prev[test_idx][-2] + Ptest22f[-1]
df.loc[test_idx, '1step_test22f'] = df.loc[test_idx, '1step_test22f'].replace(np.nan, last_forecast_test_22f)
last_forecast_train_22f = prev[train_idx][-2] + Ptrain22f[-1]
df.loc[train_idx, '1step_test22f'] = df.loc[train_idx, '1step_train22f'].replace(np.nan, last_forecast_train_22f)
last_forecast_test_23f = prev[test_idx][-2] + Ptest23f[-1]
df.loc[test_idx, '1step_test23f'] = df.loc[test_idx, '1step_test23f'].replace(np.nan, last_forecast_test_23f)
last_forecast_train_23f = prev[train_idx][-2] + Ptrain23f[-1]
df.loc[train_idx, '1step_test23f'] = df.loc[train_idx, '1step_train23f'].replace(np.nan, last_forecast_train_23f)
last_forecast_test_24f = prev[test_idx][-2] + Ptest24f[-1]
df.loc[test_idx, '1step_test24f'] = df.loc[test_idx, '1step_test24f'].replace(np.nan, last_forecast_test_24f)
last_forecast_train_24f = prev[train_idx][-2] + Ptrain24f[-1]
df.loc[train_idx, '1step_test24f'] = df.loc[train_idx, '1step_train24f'].replace(np.nan, last_forecast_train_24f)
last_forecast_test_25f = prev[test_idx][-2] + Ptest25f[-1]
df.loc[test_idx, '1step_test25f'] = df.loc[test_idx, '1step_test25f'].replace(np.nan, last_forecast_test_25f)
last_forecast_train_25f = prev[train_idx][-2] + Ptrain25f[-1]
df.loc[train_idx, '1step_test25f'] = df.loc[train_idx, '1step_train25f'].replace(np.nan, last_forecast_train_25f)
last_forecast_test_26f = prev[test_idx][-2] + Ptest26f[-1]
df.loc[test_idx, '1step_test26f'] = df.loc[test_idx, '1step_test26f'].replace(np.nan, last_forecast_test_26f)
last_forecast_train_26f = prev[train_idx][-2] + Ptrain26f[-1]
df.loc[train_idx, '1step_test26f'] = df.loc[train_idx, '1step_train26f'].replace(np.nan, last_forecast_train_26f)
last_forecast_test_27f = prev[test_idx][-2] + Ptest27f[-1]
df.loc[test_idx, '1step_test27f'] = df.loc[test_idx, '1step_test27f'].replace(np.nan, last_forecast_test_27f)
last_forecast_train_27f = prev[train_idx][-2] + Ptrain27f[-1]
df.loc[train_idx, '1step_test27f'] = df.loc[train_idx, '1step_train27f'].replace(np.nan, last_forecast_train_27f)
last_forecast_test_28f = prev[test_idx][-2] + Ptest28f[-1]
df.loc[test_idx, '1step_test28f'] = df.loc[test_idx, '1step_test28f'].replace(np.nan, last_forecast_test_28f)
last_forecast_train_28f = prev[train_idx][-2] + Ptrain28f[-1]
df.loc[train_idx, '1step_test28f'] = df.loc[train_idx, '1step_train28f'].replace(np.nan, last_forecast_train_28f)
last_forecast_test_29f = prev[test_idx][-2] + Ptest29f[-1]
df.loc[test_idx, '1step_test29f'] = df.loc[test_idx, '1step_test29f'].replace(np.nan, last_forecast_test_29f)
last_forecast_train_29f = prev[train_idx][-2] + Ptrain29f[-1]
df.loc[train_idx, '1step_test29f'] = df.loc[train_idx, '1step_train29f'].replace(np.nan, last_forecast_train_29f)
last_forecast_test_30f = prev[test_idx][-2] + Ptest30f[-1]
df.loc[test_idx, '1step_test30f'] = df.loc[test_idx, '1step_test30f'].replace(np.nan, last_forecast_test_30f)
last_forecast_train_30f = prev[train_idx][-2] + Ptrain30f[-1]
df.loc[train_idx, '1step_test30f'] = df.loc[train_idx, '1step_train30f'].replace(np.nan, last_forecast_train_30f)
last_forecast_test_31f = prev[test_idx][-2] + Ptest31f[-1]
df.loc[test_idx, '1step_test31f'] = df.loc[test_idx, '1step_test31f'].replace(np.nan, last_forecast_test_31f)
last_forecast_train_31f = prev[train_idx][-2] + Ptrain31f[-1]
df.loc[train_idx, '1step_test31f'] = df.loc[train_idx, '1step_train31f'].replace(np.nan, last_forecast_train_31f)
last_forecast_test_32f = prev[test_idx][-2] + Ptest32f[-1]
df.loc[test_idx, '1step_test32f'] = df.loc[test_idx, '1step_test32f'].replace(np.nan, last_forecast_test_32f)
last_forecast_train_32f = prev[train_idx][-2] + Ptrain32f[-1]
df.loc[train_idx, '1step_test32f'] = df.loc[train_idx, '1step_train32f'].replace(np.nan, last_forecast_train_32f)
last_forecast_test_33f = prev[test_idx][-2] + Ptest33f[-1]
df.loc[test_idx, '1step_test33f'] = df.loc[test_idx, '1step_test33f'].replace(np.nan, last_forecast_test_33f)
last_forecast_train_33f = prev[train_idx][-2] + Ptrain33f[-1]
df.loc[train_idx, '1step_test33f'] = df.loc[train_idx, '1step_train33f'].replace(np.nan, last_forecast_train_33f)
last_forecast_test_34f = prev[test_idx][-2] + Ptest34f[-1]
df.loc[test_idx, '1step_test34f'] = df.loc[test_idx, '1step_test34f'].replace(np.nan, last_forecast_test_34f)
last_forecast_train_34f = prev[train_idx][-2] + Ptrain34f[-1]
df.loc[train_idx, '1step_test34f'] = df.loc[train_idx, '1step_train34f'].replace(np.nan, last_forecast_train_34f)
last_forecast_test_35f = prev[test_idx][-2] + Ptest35f[-1]
df.loc[test_idx, '1step_test35f'] = df.loc[test_idx, '1step_test35f'].replace(np.nan, last_forecast_test_35f)
last_forecast_train_35f = prev[train_idx][-2] + Ptrain35f[-1]
df.loc[train_idx, '1step_test35f'] = df.loc[train_idx, '1step_train35f'].replace(np.nan, last_forecast_train_35f)
last_forecast_test_36f = prev[test_idx][-2] + Ptest36f[-1]
df.loc[test_idx, '1step_test36f'] = df.loc[test_idx, '1step_test36f'].replace(np.nan, last_forecast_test_36f)
last_forecast_train_36f = prev[train_idx][-2] + Ptrain36f[-1]
df.loc[train_idx, '1step_test36f'] = df.loc[train_idx, '1step_train36f'].replace(np.nan, last_forecast_train_36f)
test_log_pass = df.iloc[-Ntest:]['LogMM'] #Se encuentra arriba el mismo código pero lo usamos para recordar la serie
# MAPE RNN 1 CAPA
mape2 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test2'])
print("1-step(1c_2n RNN MAPE:", mape2)
mape3 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test3'])
print("1-step(1c_3n RNN MAPE:", mape3)
mape4 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test4'])
print("1-step(1c_4n RNN MAPE:", mape4)
mape5 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test5'])
print("1-step(1c_5n RNN MAPE:", mape5)
mape6 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test6'])
print("1-step(1c_6n RNN MAPE:", mape6)
mape7 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test7'])
print("1-step(1c_7n RNN MAPE:", mape7)
mape8 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test8'])
print("1-step(1c_8n RNN MAPE:", mape8)
mape9 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test9'])
print("1-step(1c_9n RNN MAPE:", mape9)
mape10 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test10'])
print("1-step(1c_10n RNN MAPE:", mape10)
mape11 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test11'])
print("1-step(1c_11n RNN MAPE:", mape11)
mape12 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test12'])
print("1-step(1c_12n RNN MAPE:", mape12)
mape13 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test13'])
print("1-step(1c_13n RNN MAPE:", mape13)
mape14 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test14'])
print("1-step(1c_14n RNN MAPE:", mape14)
mape15 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test15'])
print("1-step(1c_15n RNN MAPE:", mape15)
mape16 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test16'])
print("1-step(1c_16n RNN MAPE:", mape16)
mape17 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test17'])
print("1-step(1c_17n RNN MAPE:", mape17)
mape18 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test18'])
print("1-step(1c_18n RNN MAPE:", mape18)
mape19 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test19'])
print("1-step(1c_19n RNN MAPE:", mape19)
mape20 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test20'])
print("1-step(1c_20n RNN MAPE:", mape20)
mape21 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test21'])
print("1-step(1c_21n RNN MAPE:", mape21)
mape22 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test22'])
print("1-step(1c_22n RNN MAPE:", mape22)
mape23 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test23'])
print("1-step(1c_23n RNN MAPE:", mape23)
mape24 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test24'])
print("1-step(1c_24n RNN MAPE:", mape24)
mape25 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test25'])
print("1-step(1c_25n RNN MAPE:", mape25)
mape26 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test26'])
print("1-step(1c_26n RNN MAPE:", mape26)
mape27 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test27'])
print("1-step(1c_27n RNN MAPE:", mape27)
mape28 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test28'])
print("1-step(1c_28n RNN MAPE:", mape28)
mape29 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test29'])
print("1-step(1c_29n RNN MAPE:", mape29)
mape30 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test30'])
print("1-step(1c_30n RNN MAPE:", mape30)
mape31 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test31'])
print("1-step(1c_31n RNN MAPE:", mape31)
mape32 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test32'])
print("1-step(1c_32n RNN MAPE:", mape32)
mape33 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test33'])
print("1-step(1c_33n RNN MAPE:", mape33)
mape34 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test34'])
print("1-step(1c_34n RNN MAPE:", mape34)
mape35 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test35'])
print("1-step(1c_35n RNN MAPE:", mape35)
mape36 = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test36'])
print("1-step(1c_36n RNN MAPE:", mape36)
1-step(1c_2n RNN MAPE: 0.003476297837738591 1-step(1c_3n RNN MAPE: 0.003406719290379155 1-step(1c_4n RNN MAPE: 0.00208995914353786 1-step(1c_5n RNN MAPE: 0.0024903122806434388 1-step(1c_6n RNN MAPE: 0.008027522978832987 1-step(1c_7n RNN MAPE: 0.007909642524430816 1-step(1c_8n RNN MAPE: 0.01054549418642795 1-step(1c_9n RNN MAPE: 0.007778137200111827 1-step(1c_10n RNN MAPE: 0.00919970766891517 1-step(1c_11n RNN MAPE: 0.012680370519027831 1-step(1c_12n RNN MAPE: 0.010820460037035837 1-step(1c_13n RNN MAPE: 0.012472723592954647 1-step(1c_14n RNN MAPE: 0.004638916610636115 1-step(1c_15n RNN MAPE: 0.01395422470562759 1-step(1c_16n RNN MAPE: 0.01181950775042264 1-step(1c_17n RNN MAPE: 0.01008561925142397 1-step(1c_18n RNN MAPE: 0.010335755432046276 1-step(1c_19n RNN MAPE: 0.00933122912070865 1-step(1c_20n RNN MAPE: 0.009323513547178953 1-step(1c_21n RNN MAPE: 0.008117967753107434 1-step(1c_22n RNN MAPE: 0.01313243975637998 1-step(1c_23n RNN MAPE: 0.005674747032273412 1-step(1c_24n RNN MAPE: 0.008118417425043137 1-step(1c_25n RNN MAPE: 0.009154844829591516 1-step(1c_26n RNN MAPE: 0.006619893612644561 1-step(1c_27n RNN MAPE: 0.02704934598121328 1-step(1c_28n RNN MAPE: 0.0049299757637122224 1-step(1c_29n RNN MAPE: 0.014798435382487206 1-step(1c_30n RNN MAPE: 0.01811083554789952 1-step(1c_31n RNN MAPE: 0.009421329525969045 1-step(1c_32n RNN MAPE: 0.004970893738105087 1-step(1c_33n RNN MAPE: 0.009184682554742873 1-step(1c_34n RNN MAPE: 0.008896807135145433 1-step(1c_35n RNN MAPE: 0.018929101861684 1-step(1c_36n RNN MAPE: 0.020052414612019988
# MAPE RNN 2 capas
mape2b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test2b'])
print("1-step(2c_2n RNN MAPE:", mape2b)
mape3b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test3b'])
print("1-step(2c_3n RNN MAPE:", mape3b)
mape4b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test4b'])
print("1-step(2c_4n RNN MAPE:", mape4b)
mape5b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test5b'])
print("1-step(2c_5n RNN MAPE:", mape5b)
mape6b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test6b'])
print("1-step(2c_6n RNN MAPE:", mape6b)
mape7b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test7b'])
print("1-step(2c_7n RNN MAPE:", mape7b)
mape8b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test8b'])
print("1-step(2c_8n RNN MAPE:", mape8b)
mape9b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test9b'])
print("1-step(2c_9n RNN MAPE:", mape9b)
mape10b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test10b'])
print("1-step(2c_10n RNN MAPE:", mape10b)
mape11b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test11b'])
print("1-step(2c_11n RNN MAPE:", mape11b)
mape12b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test12b'])
print("1-step(2c_12n RNN MAPE:", mape12b)
mape13b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test13b'])
print("1-step(2c_13n RNN MAPE:", mape13b)
mape14b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test14b'])
print("1-step(2c_14n RNN MAPE:", mape14b)
mape15b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test15b'])
print("1-step(2c_15n RNN MAPE:", mape15b)
mape16b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test16b'])
print("1-step(2c_16n RNN MAPE:", mape16b)
mape17b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test17b'])
print("1-step(2c_17n RNN MAPE:", mape17b)
mape18b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test18b'])
print("1-step(2c_18n RNN MAPE:", mape18b)
mape19b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test19b'])
print("1-step(2c_19n RNN MAPE:", mape19b)
mape20b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test20b'])
print("1-step(2c_20n RNN MAPE:", mape20b)
mape21b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test21b'])
print("1-step(2c_21n RNN MAPE:", mape21b)
mape22b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test22b'])
print("1-step(2c_22n RNN MAPE:", mape22b)
mape23b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test23b'])
print("1-step(2c_23n RNN MAPE:", mape23b)
mape24b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test24b'])
print("1-step(2c_24n RNN MAPE:", mape24b)
mape25b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test25b'])
print("1-step(2c_25n RNN MAPE:", mape25b)
mape26b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test26b'])
print("1-step(2c_26n RNN MAPE:", mape26b)
mape27b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test27b'])
print("1-step(2c_27n RNN MAPE:", mape27b)
mape28b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test28b'])
print("1-step(2c_28n RNN MAPE:", mape28b)
mape29b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test29b'])
print("1-step(2c_29n RNN MAPE:", mape29b)
mape30b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test30b'])
print("1-step(2c_30n RNN MAPE:", mape30b)
mape31b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test31b'])
print("1-step(2c_31n RNN MAPE:", mape31b)
mape32b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test32b'])
print("1-step(2c_32n RNN MAPE:", mape32b)
mape33b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test33b'])
print("1-step(2c_33n RNN MAPE:", mape33b)
mape34b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test34b'])
print("1-step(2c_34n RNN MAPE:", mape34b)
mape35b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test35b'])
print("1-step(2c_35n RNN MAPE:", mape35b)
mape36b = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test36b'])
print("1-step(2c_36n RNN MAPE:", mape36b)
1-step(2c_2n RNN MAPE: 0.008821533695545556 1-step(2c_3n RNN MAPE: 0.00882493931413036 1-step(2c_4n RNN MAPE: 0.0024799901250260174 1-step(2c_5n RNN MAPE: 0.007195815838075842 1-step(2c_6n RNN MAPE: 0.005859900785423081 1-step(2c_7n RNN MAPE: 0.009609526717810605 1-step(2c_8n RNN MAPE: 0.007682873631328084 1-step(2c_9n RNN MAPE: 0.0075352156926351424 1-step(2c_10n RNN MAPE: 0.007060583169800356 1-step(2c_11n RNN MAPE: 0.015021830313059705 1-step(2c_12n RNN MAPE: 0.012533865694760726 1-step(2c_13n RNN MAPE: 0.018713904431525867 1-step(2c_14n RNN MAPE: 0.018633663605227002 1-step(2c_15n RNN MAPE: 0.004342478201763509 1-step(2c_16n RNN MAPE: 0.01683151418911202 1-step(2c_17n RNN MAPE: 0.006047236781182326 1-step(2c_18n RNN MAPE: 0.011744009350297999 1-step(2c_19n RNN MAPE: 0.000785666839120728 1-step(2c_20n RNN MAPE: 0.023241417778080517 1-step(2c_21n RNN MAPE: 0.015855789608195246 1-step(2c_22n RNN MAPE: 0.00893009044778724 1-step(2c_23n RNN MAPE: 0.0008254615323569478 1-step(2c_24n RNN MAPE: 0.029162981522901917 1-step(2c_25n RNN MAPE: 0.02033928633349934 1-step(2c_26n RNN MAPE: 0.004152541191223006 1-step(2c_27n RNN MAPE: 0.015301583177565393 1-step(2c_28n RNN MAPE: 0.002549677516247765 1-step(2c_29n RNN MAPE: 0.012925471132910236 1-step(2c_30n RNN MAPE: 0.0038361141619081295 1-step(2c_31n RNN MAPE: 0.012236842202429135 1-step(2c_32n RNN MAPE: 0.017937265975417897 1-step(2c_33n RNN MAPE: 0.007116396564300855 1-step(2c_34n RNN MAPE: 0.04396565508998516 1-step(2c_35n RNN MAPE: 0.03886877358943269 1-step(2c_36n RNN MAPE: 0.015780627039009926
# MAPE LSTM 1 capas
mape2c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test2c'])
print("1-step(1c_2n LSTM MAPE:", mape2c)
mape3c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test3c'])
print("1-step(1c_3n LSTM MAPE:", mape3c)
mape4c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test4c'])
print("1-step(1c_4n LSTM MAPE:", mape4c)
mape5c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test5c'])
print("1-step(1c_5n LSTM MAPE:", mape5c)
mape6c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test6c'])
print("1-step(1c_6n LSTM MAPE:", mape6c)
mape7c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test7c'])
print("1-step(1c_7n LSTM MAPE:", mape7c)
mape8c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test8c'])
print("1-step(1c_8n LSTM MAPE:", mape8c)
mape9c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test9c'])
print("1-step(1c_9n LSTM MAPE:", mape9c)
mape10c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test10c'])
print("1-step(1c_10n LSTM MAPE:", mape10c)
mape11c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test11c'])
print("1-step(1c_11n LSTM MAPE:", mape11c)
mape12c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test12c'])
print("1-step(1c_12n LSTM MAPE:", mape12c)
mape13c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test13c'])
print("1-step(1c_13n LSTM MAPE:", mape13c)
mape14c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test14c'])
print("1-step(1c_14n LSTM MAPE:", mape14c)
mape15c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test15c'])
print("1-step(1c_15n LSTM MAPE:", mape15c)
mape16c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test16c'])
print("1-step(1c_16n LSTM MAPE:", mape16c)
mape17c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test17c'])
print("1-step(1c_17n LSTM MAPE:", mape17c)
mape18c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test18c'])
print("1-step(1c_18n LSTM MAPE:", mape18c)
mape19c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test19c'])
print("1-step(1c_19n LSTM MAPE:", mape19c)
mape20c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test20c'])
print("1-step(1c_20n LSTM MAPE:", mape20c)
mape21c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test21c'])
print("1-step(1c_21n LSTM MAPE:", mape21c)
mape22c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test22c'])
print("1-step(1c_22n LSTM MAPE:", mape22c)
mape23c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test23c'])
print("1-step(1c_23n LSTM MAPE:", mape23c)
mape24c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test24c'])
print("1-step(1c_24n LSTM MAPE:", mape24c)
mape25c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test25c'])
print("1-step(1c_25n LSTM MAPE:", mape25c)
mape26c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test26c'])
print("1-step(1c_26n LSTM MAPE:", mape26c)
mape27c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test27c'])
print("1-step(1c_27n LSTM MAPE:", mape27c)
mape28c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test28c'])
print("1-step(1c_28n LSTM MAPE:", mape28c)
mape29c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test29c'])
print("1-step(1c_29n LSTM MAPE:", mape29c)
mape30c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test30c'])
print("1-step(1c_30n LSTM MAPE:", mape30c)
mape31c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test31c'])
print("1-step(1c_31n LSTM MAPE:", mape31c)
mape32c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test32c'])
print("1-step(1c_32n LSTM MAPE:", mape32c)
mape33c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test33c'])
print("1-step(1c_33n LSTM MAPE:", mape33c)
mape34c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test34c'])
print("1-step(1c_34n LSTM MAPE:", mape34c)
mape35c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test35c'])
print("1-step(1c_35n LSTM MAPE:", mape35c)
mape36c = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test36c'])
print("1-step(1c_36n LSTM MAPE:", mape36c)
1-step(1c_2n LSTM MAPE: 0.013914841565062583 1-step(1c_3n LSTM MAPE: 0.013985174277843593 1-step(1c_4n LSTM MAPE: 0.013814604415616605 1-step(1c_5n LSTM MAPE: 0.01712559674737368 1-step(1c_6n LSTM MAPE: 0.010985117756850795 1-step(1c_7n LSTM MAPE: 0.012986792534285613 1-step(1c_8n LSTM MAPE: 0.012757014918516038 1-step(1c_9n LSTM MAPE: 0.015206280238770404 1-step(1c_10n LSTM MAPE: 0.012535239376117054 1-step(1c_11n LSTM MAPE: 0.01161073759318517 1-step(1c_12n LSTM MAPE: 0.015189224011487682 1-step(1c_13n LSTM MAPE: 0.013062820295043428 1-step(1c_14n LSTM MAPE: 0.014584385849042583 1-step(1c_15n LSTM MAPE: 0.013202417816855138 1-step(1c_16n LSTM MAPE: 0.013277664818112775 1-step(1c_17n LSTM MAPE: 0.016121302861403668 1-step(1c_18n LSTM MAPE: 0.016226135240270678 1-step(1c_19n LSTM MAPE: 0.012846443661897789 1-step(1c_20n LSTM MAPE: 0.016527992231573595 1-step(1c_21n LSTM MAPE: 0.014401470879433083 1-step(1c_22n LSTM MAPE: 0.014745111311107563 1-step(1c_23n LSTM MAPE: 0.011864050886281462 1-step(1c_24n LSTM MAPE: 0.017121179716714112 1-step(1c_25n LSTM MAPE: 0.016361962727752248 1-step(1c_26n LSTM MAPE: 0.016063023860656552 1-step(1c_27n LSTM MAPE: 0.017294163008064366 1-step(1c_28n LSTM MAPE: 0.019121018808082292 1-step(1c_29n LSTM MAPE: 0.014810230258387203 1-step(1c_30n LSTM MAPE: 0.01206352118278961 1-step(1c_31n LSTM MAPE: 0.017458283777846403 1-step(1c_32n LSTM MAPE: 0.012930139562436806 1-step(1c_33n LSTM MAPE: 0.02014860266456184 1-step(1c_34n LSTM MAPE: 0.01591875885799323 1-step(1c_35n LSTM MAPE: 0.016586487530213828 1-step(1c_36n LSTM MAPE: 0.014694271819473925
# MAPE LSTM 2 capas
mape2d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test2d'])
print("1-step(2c_2n LSTM MAPE:", mape2d)
mape3d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test3d'])
print("1-step(2c_3n LSTM MAPE:", mape3d)
mape4d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test4d'])
print("1-step(2c_4n LSTM MAPE:", mape4d)
mape5d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test5d'])
print("1-step(2c_5n LSTM MAPE:", mape5d)
mape6d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test6d'])
print("1-step(2c_6n LSTM MAPE:", mape6d)
mape7d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test7d'])
print("1-step(2c_7n LSTM MAPE:", mape7d)
mape8d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test8d'])
print("1-step(2c_8n LSTM MAPE:", mape8d)
mape9d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test9d'])
print("1-step(2c_9n LSTM MAPE:", mape9d)
mape10d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test10d'])
print("1-step(2c_10n LSTM MAPE:", mape10d)
mape11d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test11d'])
print("1-step(2c_11n LSTM MAPE:", mape11d)
mape12d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test12d'])
print("1-step(2c_12n LSTM MAPE:", mape12d)
mape13d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test13d'])
print("1-step(2c_13n LSTM MAPE:", mape13d)
mape14d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test14d'])
print("1-step(2c_14n LSTM MAPE:", mape14d)
mape15d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test15d'])
print("1-step(2c_15n LSTM MAPE:", mape15d)
mape16d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test16d'])
print("1-step(2c_16n LSTM MAPE:", mape16d)
mape17d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test17d'])
print("1-step(2c_17n LSTM MAPE:", mape17d)
mape18d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test18d'])
print("1-step(2c_18n LSTM MAPE:", mape18d)
mape19d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test19d'])
print("1-step(2c_19n LSTM MAPE:", mape19d)
mape20d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test20d'])
print("1-step(2c_20n LSTM MAPE:", mape20d)
mape21d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test21d'])
print("1-step(2c_21n LSTM MAPE:", mape21d)
mape22d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test22d'])
print("1-step(2c_22n LSTM MAPE:", mape22d)
mape23d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test23d'])
print("1-step(2c_23n LSTM MAPE:", mape23d)
mape24d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test24d'])
print("1-step(2c_24n LSTM MAPE:", mape24d)
mape25d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test25d'])
print("1-step(2c_25n LSTM MAPE:", mape25d)
mape26d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test26d'])
print("1-step(2c_26n LSTM MAPE:", mape26d)
mape27d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test27d'])
print("1-step(2c_27n LSTM MAPE:", mape27d)
mape28d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test28d'])
print("1-step(2c_28n LSTM MAPE:", mape28d)
mape29d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test29d'])
print("1-step(2c_29n LSTM MAPE:", mape29d)
mape30d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test30d'])
print("1-step(2c_30n LSTM MAPE:", mape30d)
mape31d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test31d'])
print("1-step(2c_31n LSTM MAPE:", mape31d)
mape32d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test32d'])
print("1-step(2c_32n LSTM MAPE:", mape32d)
mape33d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test33d'])
print("1-step(2c_33n LSTM MAPE:", mape33d)
mape34d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test34d'])
print("1-step(2c_34n LSTM MAPE:", mape34d)
mape35d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test35d'])
print("1-step(2c_35n LSTM MAPE:", mape35d)
mape36d = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test36d'])
print("1-step(2c_36n LSTM MAPE:", mape36d)
1-step(2c_2n LSTM MAPE: 0.010895934357663135 1-step(2c_3n LSTM MAPE: 0.0108757051752294 1-step(2c_4n LSTM MAPE: 0.0002169355629570848 1-step(2c_5n LSTM MAPE: 0.0008816481394416963 1-step(2c_6n LSTM MAPE: 0.009964491482600044 1-step(2c_7n LSTM MAPE: 0.011471483491712542 1-step(2c_8n LSTM MAPE: 0.014774082896011457 1-step(2c_9n LSTM MAPE: 0.013943809203133732 1-step(2c_10n LSTM MAPE: 0.01842888831828752 1-step(2c_11n LSTM MAPE: 0.015337654645943553 1-step(2c_12n LSTM MAPE: 0.01311279838980445 1-step(2c_13n LSTM MAPE: 0.016741242980693613 1-step(2c_14n LSTM MAPE: 0.012868418768899128 1-step(2c_15n LSTM MAPE: 0.014708819750136394 1-step(2c_16n LSTM MAPE: 0.01588242081194855 1-step(2c_17n LSTM MAPE: 0.01814776746447533 1-step(2c_18n LSTM MAPE: 0.015804844813639963 1-step(2c_19n LSTM MAPE: 0.018140652402201543 1-step(2c_20n LSTM MAPE: 0.01912466361731641 1-step(2c_21n LSTM MAPE: 0.014618886311670694 1-step(2c_22n LSTM MAPE: 0.018729284350136887 1-step(2c_23n LSTM MAPE: 0.020797306608937284 1-step(2c_24n LSTM MAPE: 0.01940580723932788 1-step(2c_25n LSTM MAPE: 0.015203184712343821 1-step(2c_26n LSTM MAPE: 0.014900361969921607 1-step(2c_27n LSTM MAPE: 0.01805198354747063 1-step(2c_28n LSTM MAPE: 0.017561744372707033 1-step(2c_29n LSTM MAPE: 0.015821968396845538 1-step(2c_30n LSTM MAPE: 0.017960110068691606 1-step(2c_31n LSTM MAPE: 0.01844542941506162 1-step(2c_32n LSTM MAPE: 0.020136019439761902 1-step(2c_33n LSTM MAPE: 0.020271877284842508 1-step(2c_34n LSTM MAPE: 0.016041589498388027 1-step(2c_35n LSTM MAPE: 0.021175065187227036 1-step(2c_36n LSTM MAPE: 0.01955454619314895
#MAPE BILSTM 1 CAPA
mape2e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test2e'])
print("1-step(1c_2n BILSTM MAPE:", mape2e)
mape3e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test3e'])
print("1-step(1c_3n BILSTM MAPE:", mape3e)
mape4e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test4e'])
print("1-step(1c_4n BILSTM MAPE:", mape4e)
mape5e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test5e'])
print("1-step(1c_5n BILSTM MAPE:", mape5e)
mape6e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test6e'])
print("1-step(1c_6n BILSTM MAPE:", mape6e)
mape7e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test7e'])
print("1-step(1c_7n BILSTM MAPE:", mape7e)
mape8e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test8e'])
print("1-step(1c_8n BILSTM MAPE:", mape8e)
mape9e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test9e'])
print("1-step(1c_9n BILSTM MAPE:", mape9e)
mape10e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test10e'])
print("1-step(1c_10n BILSTM MAPE:", mape10e)
mape11e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test11e'])
print("1-step(1c_11n BILSTM MAPE:", mape11e)
mape12e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test12e'])
print("1-step(1c_12n BILSTM MAPE:", mape12e)
mape13e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test13e'])
print("1-step(1c_13n BILSTM MAPE:", mape13e)
mape14e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test14e'])
print("1-step(1c_14n BILSTM MAPE:", mape14e)
mape15e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test15e'])
print("1-step(1c_15n BILSTM MAPE:", mape15e)
mape16e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test16e'])
print("1-step(1c_16n BILSTM MAPE:", mape16e)
mape17e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test17e'])
print("1-step(1c_17n BILSTM MAPE:", mape17e)
mape18e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test18e'])
print("1-step(1c_18n BILSTM MAPE:", mape18e)
mape19e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test19e'])
print("1-step(1c_19n BILSTM MAPE:", mape19e)
mape20e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test20e'])
print("1-step(1c_20n BILSTM MAPE:", mape20e)
mape21e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test21e'])
print("1-step(1c_21n BILSTM MAPE:", mape21e)
mape22e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test22e'])
print("1-step(1c_22n BILSTM MAPE:", mape22e)
mape23e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test23e'])
print("1-step(1c_23n BILSTM MAPE:", mape23e)
mape24e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test24e'])
print("1-step(1c_24n BILSTM MAPE:", mape24e)
mape25e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test25e'])
print("1-step(1c_25n BILSTM MAPE:", mape25e)
mape26e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test26e'])
print("1-step(1c_26n BILSTM MAPE:", mape26e)
mape27e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test27e'])
print("1-step(1c_27n BILSTM MAPE:", mape27e)
mape28e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test28e'])
print("1-step(1c_28n BILSTM MAPE:", mape28e)
mape29e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test29e'])
print("1-step(1c_29n BILSTM MAPE:", mape29e)
mape30e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test30e'])
print("1-step(1c_30n BILSTM MAPE:", mape30e)
mape31e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test31e'])
print("1-step(1c_31n BILSTM MAPE:", mape31e)
mape32e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test32e'])
print("1-step(1c_32n BILSTM MAPE:", mape32e)
mape33e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test33e'])
print("1-step(1c_33n BILSTM MAPE:", mape33e)
mape34e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test34e'])
print("1-step(1c_34n BILSTM MAPE:", mape34e)
mape35e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test35e'])
print("1-sep(1c_35n BILSTM MAPE:", mape35e)
mape36e = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test36e'])
print("1-step(1c_36n BILSTM MAPE:", mape36e)
1-step(1c_2n BILSTM MAPE: 0.010634358644651605 1-step(1c_3n BILSTM MAPE: 0.01063234348256671 1-step(1c_4n BILSTM MAPE: 0.013632099273594073 1-step(1c_5n BILSTM MAPE: 0.016438033178883704 1-step(1c_6n BILSTM MAPE: 0.014992697453410942 1-step(1c_7n BILSTM MAPE: 0.014954931651536647 1-step(1c_8n BILSTM MAPE: 0.013576708039455154 1-step(1c_9n BILSTM MAPE: 0.014229132687062312 1-step(1c_10n BILSTM MAPE: 0.01617537733468445 1-step(1c_11n BILSTM MAPE: 0.01546895600514403 1-step(1c_12n BILSTM MAPE: 0.016347095093624947 1-step(1c_13n BILSTM MAPE: 0.017814630762066897 1-step(1c_14n BILSTM MAPE: 0.014927856467897462 1-step(1c_15n BILSTM MAPE: 0.015970180836058355 1-step(1c_16n BILSTM MAPE: 0.015102739955212306 1-step(1c_17n BILSTM MAPE: 0.014223348615771207 1-step(1c_18n BILSTM MAPE: 0.0173360261371334 1-step(1c_19n BILSTM MAPE: 0.01936940658073518 1-step(1c_20n BILSTM MAPE: 0.01755683972311297 1-step(1c_21n BILSTM MAPE: 0.020003709639068487 1-step(1c_22n BILSTM MAPE: 0.017375644701223786 1-step(1c_23n BILSTM MAPE: 0.019052310074716777 1-step(1c_24n BILSTM MAPE: 0.017693342667173064 1-step(1c_25n BILSTM MAPE: 0.014424887023713601 1-step(1c_26n BILSTM MAPE: 0.017110452100155174 1-step(1c_27n BILSTM MAPE: 0.019018799080082208 1-step(1c_28n BILSTM MAPE: 0.019401874032902926 1-step(1c_29n BILSTM MAPE: 0.025052770331741338 1-step(1c_30n BILSTM MAPE: 0.016046729419374607 1-step(1c_31n BILSTM MAPE: 0.02049725779212688 1-step(1c_32n BILSTM MAPE: 0.019589387230091234 1-step(1c_33n BILSTM MAPE: 0.016342298592977447 1-step(1c_34n BILSTM MAPE: 0.024679197307418525 1-sep(1c_35n BILSTM MAPE: 0.022568695280818705 1-step(1c_36n BILSTM MAPE: 0.022069221703898696
# MAPE BI-LSTM 2 Capas
mape2f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test2f'])
print("1-step(2c_2n BILSTM MAPE:", mape2f)
mape3f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test3f'])
print("1-step(2c_3n BILSTM MAPE:", mape3f)
mape4f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test4f'])
print("1-step(2c_4n BILSTM MAPE:", mape4f)
mape5f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test5f'])
print("1-step(2c_5n BILSTM MAPE:", mape5f)
mape6f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test6f'])
print("1-step(2c_6n BILSTM MAPE:", mape6f)
mape7f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test7f'])
print("1-step(2c_7n BILSTM MAPE:", mape7f)
mape8f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test8f'])
print("1-step(2c_8n BILSTM MAPE:", mape8f)
mape9f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test9f'])
print("1-step(2c_9n BILSTM MAPE:", mape9f)
mape10f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test10f'])
print("1-step(2c_10n BILSTM MAPE:", mape10f)
mape11f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test11f'])
print("1-step(2c_11n BILSTM MAPE:", mape11f)
mape12f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test12f'])
print("1-step(2c_12n BILSTM MAPE:", mape12f)
mape13f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test13f'])
print("1-step(2c_13n BILSTM MAPE:", mape13f)
mape14f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test14f'])
print("1-step(2c_14n BILSTM MAPE:", mape14f)
mape15f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test15f'])
print("1-step(2c_15n BILSTM MAPE:", mape15f)
mape16f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test16f'])
print("1-step(2c_16n BILSTM MAPE:", mape16f)
mape17f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test17f'])
print("1-step(2c_17n BILSTM MAPE:", mape17f)
mape18f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test18f'])
print("1-step(2c_18n BILSTM MAPE:", mape18f)
mape19f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test19f'])
print("1-step(2c_19n BILSTM MAPE:", mape19f)
mape20f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test20f'])
print("1-step(2c_20n BILSTM MAPE:", mape20f)
mape21f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test21f'])
print("1-step(2c_21n BILSTM MAPE:", mape21f)
mape22f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test22f'])
print("1-step(2c_22n BILSTM MAPE:", mape22f)
mape23f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test23f'])
print("1-step(2c_23n BILSTM MAPE:", mape23f)
mape24f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test24f'])
print("1-step(2c_24n BILSTM MAPE:", mape24f)
mape25f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test25f'])
print("1-step(2c_25n BILSTM MAPE:", mape25f)
mape26f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test26f'])
print("1-step(2c_26n BILSTM MAPE:", mape26f)
mape27f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test27f'])
print("1-step(2c_27n BILSTM MAPE:", mape27f)
mape28f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test28f'])
print("1-step(2c_28n BILSTM MAPE:", mape28f)
mape29f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test29f'])
print("1-step(2c_29n BILSTM MAPE:", mape29f)
mape30f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test30f'])
print("1-step(2c_30n BILSTM MAPE:", mape30f)
mape31f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test31f'])
print("1-step(2c_31n BILSTM MAPE:", mape31f)
mape32f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test32f'])
print("1-step(2c_32n BILSTM MAPE:", mape32f)
mape33f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test33f'])
print("1-step(2c_33n BILSTM MAPE:", mape33f)
mape34f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test34f'])
print("1-step(2c_34n BILSTM MAPE:", mape34f)
mape35f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test35f'])
print("1-step(2c_35n BILSTM MAPE:", mape35f)
mape36f = mean_absolute_percentage_error(
test_log_pass, df.loc[test_idx, '1step_test36f'])
print("1-step(2c_36n BILSTM MAPE:", mape36f)
1-step(2c_2n BILSTM MAPE: 0.01509251497550671 1-step(2c_3n BILSTM MAPE: 0.015157227102104974 1-step(2c_4n BILSTM MAPE: 0.015482842709352525 1-step(2c_5n BILSTM MAPE: 0.014896371842998468 1-step(2c_6n BILSTM MAPE: 0.014783036490376789 1-step(2c_7n BILSTM MAPE: 0.014052491305028354 1-step(2c_8n BILSTM MAPE: 0.014185707090317962 1-step(2c_9n BILSTM MAPE: 0.014669560733859575 1-step(2c_10n BILSTM MAPE: 0.013628281805515443 1-step(2c_11n BILSTM MAPE: 0.017094358777966848 1-step(2c_12n BILSTM MAPE: 0.01720541256728605 1-step(2c_13n BILSTM MAPE: 0.016409356631895432 1-step(2c_14n BILSTM MAPE: 0.018391622468122325 1-step(2c_15n BILSTM MAPE: 0.016550796480498575 1-step(2c_16n BILSTM MAPE: 0.015308817772885383 1-step(2c_17n BILSTM MAPE: 0.015818849153544715 1-step(2c_18n BILSTM MAPE: 0.02133465129065324 1-step(2c_19n BILSTM MAPE: 0.0165090984208743 1-step(2c_20n BILSTM MAPE: 0.018552383031161194 1-step(2c_21n BILSTM MAPE: 0.017422666724778792 1-step(2c_22n BILSTM MAPE: 0.017673299062410327 1-step(2c_23n BILSTM MAPE: 0.014043827056528822 1-step(2c_24n BILSTM MAPE: 0.013772709031598602 1-step(2c_25n BILSTM MAPE: 0.014482697379025612 1-step(2c_26n BILSTM MAPE: 0.01311260865481048 1-step(2c_27n BILSTM MAPE: 0.011280067445698325 1-step(2c_28n BILSTM MAPE: 0.011086301531783811 1-step(2c_29n BILSTM MAPE: 0.016690538200905693 1-step(2c_30n BILSTM MAPE: 0.014298592771003935 1-step(2c_31n BILSTM MAPE: 0.013868288034809814 1-step(2c_32n BILSTM MAPE: 0.012689331702792924 1-step(2c_33n BILSTM MAPE: 0.008355940023266471 1-step(2c_34n BILSTM MAPE: 0.01766982311732083 1-step(2c_35n BILSTM MAPE: 0.008365741733054842 1-step(2c_36n BILSTM MAPE: 0.01625514761819828
#RMSE Y MSE RNN 1 CAPA
MSE2 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test2'])
RMSE2 = math.sqrt(MSE2)
print("1-step(1c_2n RNN RMSE:", RMSE2)
MSE3 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test3'])
RMSE3 = math.sqrt(MSE3)
print("1-step(1c_2n RNN RMSE:", RMSE3)
MSE4 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test4'])
RMSE4 = math.sqrt(MSE4)
print("1-step(1c_2n RNN RMSE:", RMSE4)
MSE5 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test5'])
RMSE5 = math.sqrt(MSE5)
print("1-step(1c_2n RNN RMSE:", RMSE5)
MSE6 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test6'])
RMSE6 = math.sqrt(MSE6)
print("1-step(1c_2n RNN RMSE:", RMSE6)
MSE7 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test7'])
RMSE7 = math.sqrt(MSE7)
print("1-step(1c_2n RNN RMSE:", RMSE7)
MSE8 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test8'])
RMSE8 = math.sqrt(MSE8)
print("1-step(1c_2n RNN RMSE:", RMSE8)
MSE9 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test9'])
RMSE9 = math.sqrt(MSE9)
print("1-step(1c_2n RNN RMSE:", RMSE9)
MSE10 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test10'])
RMSE10 = math.sqrt(MSE10)
print("1-step(1c_2n RNN RMSE:", RMSE10)
MSE11 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test11'])
RMSE11 = math.sqrt(MSE11)
print("1-step(1c_2n RNN RMSE:", RMSE11)
MSE12 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test12'])
RMSE12 = math.sqrt(MSE12)
print("1-step(1c_2n RNN RMSE:", RMSE12)
MSE13 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test13'])
RMSE13 = math.sqrt(MSE13)
print("1-step(1c_2n RNN RMSE:", RMSE13)
MSE14 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test14'])
RMSE14 = math.sqrt(MSE14)
print("1-step(1c_2n RNN RMSE:", RMSE14)
MSE15 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test15'])
RMSE15 = math.sqrt(MSE15)
print("1-step(1c_2n RNN RMSE:", RMSE15)
MSE16 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test16'])
RMSE16 = math.sqrt(MSE16)
print("1-step(1c_2n RNN RMSE:", RMSE16)
MSE17 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test17'])
RMSE17 = math.sqrt(MSE17)
print("1-step(1c_2n RNN RMSE:", RMSE17)
MSE18 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test18'])
RMSE18 = math.sqrt(MSE18)
print("1-step(1c_2n RNN RMSE:", RMSE18)
MSE19 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test19'])
RMSE19 = math.sqrt(MSE19)
print("1-step(1c_2n RNN RMSE:", RMSE19)
MSE20 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test20'])
RMSE20 = math.sqrt(MSE20)
print("1-step(1c_2n RNN RMSE:", RMSE20)
MSE21 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test21'])
RMSE21 = math.sqrt(MSE21)
print("1-step(1c_2n RNN RMSE:", RMSE21)
MSE22 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test22'])
RMSE22 = math.sqrt(MSE22)
print("1-step(1c_2n RNN RMSE:", RMSE22)
MSE23 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test23'])
RMSE23 = math.sqrt(MSE23)
print("1-step(1c_2n RNN RMSE:", RMSE23)
MSE24 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test24'])
RMSE24 = math.sqrt(MSE24)
print("1-step(1c_2n RNN RMSE:", RMSE24)
MSE25 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test25'])
RMSE25 = math.sqrt(MSE25)
print("1-step(1c_2n RNN RMSE:", RMSE25)
MSE26 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test26'])
RMSE26 = math.sqrt(MSE26)
print("1-step(1c_2n RNN RMSE:", RMSE26)
MSE27 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test27'])
RMSE27 = math.sqrt(MSE27)
print("1-step(1c_2n RNN RMSE:", RMSE27)
MSE28 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test28'])
RMSE28 = math.sqrt(MSE28)
print("1-step(1c_2n RNN RMSE:", RMSE28)
MSE29 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test29'])
RMSE29 = math.sqrt(MSE29)
print("1-step(1c_2n RNN RMSE:", RMSE29)
MSE30 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test30'])
RMSE30 = math.sqrt(MSE30)
print("1-step(1c_2n RNN RMSE:", RMSE30)
MSE31 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test31'])
RMSE31 = math.sqrt(MSE31)
print("1-step(1c_2n RNN RMSE:", RMSE31)
MSE32 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test32'])
RMSE32 = math.sqrt(MSE32)
print("1-step(1c_2n RNN RMSE:", RMSE32)
MSE33 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test33'])
RMSE33 = math.sqrt(MSE33)
print("1-step(1c_2n RNN RMSE:", RMSE33)
MSE34 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test34'])
RMSE34 = math.sqrt(MSE34)
print("1-step(1c_2n RNN RMSE:", RMSE34)
MSE35 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test35'])
RMSE35 = math.sqrt(MSE35)
print("1-step(1c_2n RNN RMSE:", RMSE35)
MSE36 = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test36'])
RMSE36 = math.sqrt(MSE36)
print("1-step(1c_2n RNN RMSE:", RMSE36)
1-step(1c_2n RNN RMSE: 0.013959740831345636 1-step(1c_2n RNN RMSE: 0.013377705257171212 1-step(1c_2n RNN RMSE: 0.008328520583599806 1-step(1c_2n RNN RMSE: 0.00983576927620217 1-step(1c_2n RNN RMSE: 0.03162472103783825 1-step(1c_2n RNN RMSE: 0.03116423739932239 1-step(1c_2n RNN RMSE: 0.04147366065165851 1-step(1c_2n RNN RMSE: 0.03065061589714017 1-step(1c_2n RNN RMSE: 0.03620704712161823 1-step(1c_2n RNN RMSE: 0.04983615378347182 1-step(1c_2n RNN RMSE: 0.04255026581768837 1-step(1c_2n RNN RMSE: 0.04902247087338465 1-step(1c_2n RNN RMSE: 0.018232243304728134 1-step(1c_2n RNN RMSE: 0.05482897792844967 1-step(1c_2n RNN RMSE: 0.04646317812250337 1-step(1c_2n RNN RMSE: 0.03967344131670329 1-step(1c_2n RNN RMSE: 0.04065255856977886 1-step(1c_2n RNN RMSE: 0.036721515507568935 1-step(1c_2n RNN RMSE: 0.036691333190280916 1-step(1c_2n RNN RMSE: 0.03197807824695096 1-step(1c_2n RNN RMSE: 0.05160781415470554 1-step(1c_2n RNN RMSE: 0.0223490039685729 1-step(1c_2n RNN RMSE: 0.031979835162759415 1-step(1c_2n RNN RMSE: 0.03603157159776452 1-step(1c_2n RNN RMSE: 0.026119971551570056 1-step(1c_2n RNN RMSE: 0.1062060738990568 1-step(1c_2n RNN RMSE: 0.019386737196764512 1-step(1c_2n RNN RMSE: 0.05813871985591649 1-step(1c_2n RNN RMSE: 0.07112983867180488 1-step(1c_2n RNN RMSE: 0.03707399048463245 1-step(1c_2n RNN RMSE: 0.019549202690895915 1-step(1c_2n RNN RMSE: 0.03614827746892571 1-step(1c_2n RNN RMSE: 0.03502242382374839 1-step(1c_2n RNN RMSE: 0.07433991976393525 1-step(1c_2n RNN RMSE: 0.07874712030165543
#RMSE Y MSE RNN 2 CAPA
MSE2b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test2b'])
RMSE2b = math.sqrt(MSE2b)
print("1-step(2c_2n RNN RMSE:", RMSE2b)
MSE3b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test3b'])
RMSE3b = math.sqrt(MSE3b)
print("1-step(2c_3n RNN RMSE:", RMSE3b)
MSE4b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test4b'])
RMSE4b = math.sqrt(MSE4b)
print("1-step(2c_4n RNN RMSE:", RMSE4b)
MSE5b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test5b'])
RMSE5b = math.sqrt(MSE5b)
print("1-step(2c_5n RNN RMSE:", RMSE5b)
MSE6b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test6b'])
RMSE6b = math.sqrt(MSE6b)
print("1-step(2c_6n RNN RMSE:", RMSE6b)
MSE7b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test7b'])
RMSE7b = math.sqrt(MSE7b)
print("1-step(2c_7n RNN RMSE:", RMSE7b)
MSE8b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test8b'])
RMSE8b = math.sqrt(MSE8b)
print("1-step(2c_8n RNN RMSE:", RMSE8b)
MSE9b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test9b'])
RMSE9b = math.sqrt(MSE9b)
print("1-step(2c_9n RNN RMSE:", RMSE9b)
MSE10b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test10b'])
RMSE10b = math.sqrt(MSE10b)
print("1-step(2c_10n RNN RMSE:", RMSE10b)
MSE11b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test11b'])
RMSE11b = math.sqrt(MSE11b)
print("1-step(2c_11n RNN RMSE:", RMSE11b)
MSE12b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test12b'])
RMSE12b = math.sqrt(MSE12b)
print("1-step(2c_12n RNN RMSE:", RMSE12b)
MSE13b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test13b'])
RMSE13b = math.sqrt(MSE13b)
print("1-step(2c_13n RNN RMSE:", RMSE13b)
MSE14b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test14b'])
RMSE14b = math.sqrt(MSE14b)
print("1-step(2c_13n RNN RMSE:", RMSE14b)
MSE15b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test15b'])
RMSE15b = math.sqrt(MSE15b)
print("1-step(2c_15n RNN RMSE:", RMSE15b)
MSE16b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test16b'])
RMSE16b = math.sqrt(MSE16b)
print("1-step(2c_16n RNN RMSE:", RMSE16b)
MSE17b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test17b'])
RMSE17b = math.sqrt(MSE17b)
print("1-step(2c_17n RNN RMSE:", RMSE17b)
MSE18b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test18b'])
RMSE18b = math.sqrt(MSE18b)
print("1-step(2c_18n RNN RMSE:", RMSE18b)
MSE19b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test19b'])
RMSE19b = math.sqrt(MSE19b)
print("1-step(2c_19n RNN RMSE:", RMSE19b)
MSE20b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test20b'])
RMSE20b = math.sqrt(MSE20b)
print("1-step(2c_20n RNN RMSE:", RMSE20b)
MSE21b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test21b'])
RMSE21b = math.sqrt(MSE21b)
print("1-step(2c_21n RNN RMSE:", RMSE21b)
MSE22b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test22b'])
RMSE22b = math.sqrt(MSE22b)
print("1-step(2c_22n RNN RMSE:", RMSE22b)
MSE23b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test23b'])
RMSE23b = math.sqrt(MSE23b)
print("1-step(2c_23n RNN RMSE:", RMSE23b)
MSE24b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test24b'])
RMSE24b = math.sqrt(MSE24b)
print("1-step(2c_24n RNN RMSE:", RMSE24b)
MSE25b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test25b'])
RMSE25b = math.sqrt(MSE25b)
print("1-step(2c_25n RNN RMSE:", RMSE25b)
MSE26b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test26b'])
RMSE26b = math.sqrt(MSE26b)
print("1-step(2c_26n RNN RMSE:", RMSE26b)
MSE27b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test27b'])
RMSE27b = math.sqrt(MSE27b)
print("1-step(2c_27n RNN RMSE:", RMSE27b)
MSE28b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test28b'])
RMSE28b = math.sqrt(MSE28b)
print("1-step(2c_28n RNN RMSE:", RMSE28b)
MSE29b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test29b'])
RMSE29b = math.sqrt(MSE29b)
print("1-step(2c_29n RNN RMSE:", RMSE29b)
MSE30b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test30b'])
RMSE30b = math.sqrt(MSE30b)
print("1-step(2c_30n RNN RMSE:", RMSE30b)
MSE31b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test31b'])
RMSE31b = math.sqrt(MSE31b)
print("1-step(2c_31n RNN RMSE:", RMSE31b)
MSE32b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test32b'])
RMSE32b = math.sqrt(MSE32b)
print("1-step(2c_32n RNN RMSE:", RMSE32b)
MSE33b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test33b'])
RMSE33b = math.sqrt(MSE33b)
print("1-step(2c_33n RNN RMSE:", RMSE33b)
MSE34b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test34b'])
RMSE34b = math.sqrt(MSE34b)
print("1-step(2c_34n RNN RMSE:", RMSE34b)
MSE35b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test35b'])
RMSE35b = math.sqrt(MSE35b)
print("1-step(2c_35n RNN RMSE:", RMSE35b)
MSE36b = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test36b'])
RMSE36b = math.sqrt(MSE36b)
print("1-step(2c_36n RNN RMSE:", RMSE36b)
1-step(2c_2n RNN RMSE: 0.034737915200686115 1-step(2c_3n RNN RMSE: 0.03474140200243962 1-step(2c_4n RNN RMSE: 0.009796491426353566 1-step(2c_5n RNN RMSE: 0.028377450196383536 1-step(2c_6n RNN RMSE: 0.02308686752111456 1-step(2c_7n RNN RMSE: 0.03781030288838305 1-step(2c_8n RNN RMSE: 0.03027860316857787 1-step(2c_9n RNN RMSE: 0.02970208859332236 1-step(2c_10n RNN RMSE: 0.02784986882521201 1-step(2c_11n RNN RMSE: 0.05901464357237655 1-step(2c_12n RNN RMSE: 0.049262055744015515 1-step(2c_13n RNN RMSE: 0.07349566797791027 1-step(2c_13n RNN RMSE: 0.07318087587043567 1-step(2c_15n RNN RMSE: 0.017058811287550112 1-step(2c_16n RNN RMSE: 0.06623252875871961 1-step(2c_17n RNN RMSE: 0.02383389884713236 1-step(2c_18n RNN RMSE: 0.04616741753834998 1-step(2c_19n RNN RMSE: 0.004024418256100607 1-step(2c_20n RNN RMSE: 0.09126087630326735 1-step(2c_21n RNN RMSE: 0.06228489492843249 1-step(2c_22n RNN RMSE: 0.035152576675958426 1-step(2c_23n RNN RMSE: 0.004123443443727881 1-step(2c_24n RNN RMSE: 0.11462346422436066 1-step(2c_25n RNN RMSE: 0.07987269998767174 1-step(2c_26n RNN RMSE: 0.016308470685642465 1-step(2c_27n RNN RMSE: 0.060111598714741254 1-step(2c_28n RNN RMSE: 0.010062020131708722 1-step(2c_29n RNN RMSE: 0.05079667296115975 1-step(2c_30n RNN RMSE: 0.015061610875648888 1-step(2c_31n RNN RMSE: 0.0480982190708141 1-step(2c_32n RNN RMSE: 0.07044895702201344 1-step(2c_33n RNN RMSE: 0.028067597070032525 1-step(2c_34n RNN RMSE: 0.1726150585899162 1-step(2c_35n RNN RMSE: 0.15260433924089717 1-step(2c_36n RNN RMSE: 0.06199013594903349
#RMSE Y MSE LSTM1 CAPA
MSE2c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test2c'])
RMSE2c = math.sqrt(MSE2c)
print("1-step(1c_2n LSTM RMSE :", RMSE2c)
MSE3c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test3c'])
RMSE3c = math.sqrt(MSE3c)
print("1-step(1c_3n LSTM RMSE :", RMSE3c)
MSE4c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test4c'])
RMSE4c = math.sqrt(MSE4c)
print("1-step(1c_4n LSTM RMSE :", RMSE4c)
MSE5c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test5c'])
RMSE5c = math.sqrt(MSE5c)
print("1-step(1c_5n LSTM RMSE :", RMSE5c)
MSE6c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test6c'])
RMSE6c = math.sqrt(MSE6c)
print("1-step(1c_6n LSTM RMSE :", RMSE6c)
MSE7c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test7c'])
RMSE7c = math.sqrt(MSE7c)
print("1-step(1c_7n LSTM RMSE :", RMSE7c)
MSE8c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test8c'])
RMSE8c = math.sqrt(MSE8c)
print("1-step(1c_8n LSTM RMSE :", RMSE8c)
MSE9c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test9c'])
RMSE9c = math.sqrt(MSE9c)
print("1-step(1c_9n LSTM RMSE :", RMSE9c)
MSE10c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test10c'])
RMSE10c = math.sqrt(MSE10c)
print("1-step(1c_10n LSTM RMSE :", RMSE10c)
MSE11c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test11c'])
RMSE11c = math.sqrt(MSE11c)
print("1-step(1c_11n LSTM RMSE :", RMSE11c)
MSE12c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test12c'])
RMSE12c = math.sqrt(MSE12c)
print("1-step(1c_12n LSTM RMSE :", RMSE12c)
MSE13c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test13c'])
RMSE13c = math.sqrt(MSE13c)
print("1-step(1c_13n LSTM RMSE :", RMSE13c)
MSE14c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test14c'])
RMSE14c = math.sqrt(MSE14c)
print("1-step(1c_14n LSTM RMSE :", RMSE14c)
MSE15c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test15c'])
RMSE15c = math.sqrt(MSE15c)
print("1-step(1c_15n LSTM RMSE :", RMSE15c)
MSE16c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test16c'])
RMSE16c = math.sqrt(MSE16c)
print("1-step(1c_16n LSTM RMSE :", RMSE16c)
MSE17c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test17c'])
RMSE17c = math.sqrt(MSE17c)
print("1-step(1c_17n LSTM RMSE :", RMSE17c)
MSE18c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test18c'])
RMSE18c = math.sqrt(MSE18c)
print("1-step(1c_18n LSTM RMSE :", RMSE18c)
MSE19c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test19c'])
RMSE19c = math.sqrt(MSE19c)
print("1-step(1c_19n LSTM RMSE :", RMSE19c)
MSE20c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test20c'])
RMSE20c = math.sqrt(MSE20c)
print("1-step(1c_20n LSTM RMSE :", RMSE20c)
MSE21c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test21c'])
RMSE21c = math.sqrt(MSE21c)
print("1-step(1c_21n LSTM RMSE :", RMSE21c)
MSE22c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test22c'])
RMSE22c = math.sqrt(MSE22c)
print("1-step(1c_22n LSTM RMSE :", RMSE22c)
MSE23c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test23c'])
RMSE23c = math.sqrt(MSE23c)
print("1-step(1c_23n LSTM RMSE :", RMSE23c)
MSE24c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test24c'])
RMSE24c = math.sqrt(MSE24c)
print("1-step(1c_24n LSTM RMSE :", RMSE24c)
MSE25c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test25c'])
RMSE25c = math.sqrt(MSE25c)
print("1-step(1c_25n LSTM RMSE :", RMSE25c)
MSE26c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test26c'])
RMSE26c = math.sqrt(MSE26c)
print("1-step(1c_26n LSTM RMSE :", RMSE26c)
MSE27c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test27c'])
RMSE27c = math.sqrt(MSE27c)
print("1-step(1c_27n LSTM RMSE :", RMSE27c)
MSE28c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test28c'])
RMSE28c = math.sqrt(MSE28c)
print("1-step(1c_28n LSTM RMSE :", RMSE28c)
MSE29c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test29c'])
RMSE29c = math.sqrt(MSE29c)
print("1-step(1c_29n LSTM RMSE :", RMSE29c)
MSE30c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test30c'])
RMSE30c = math.sqrt(MSE30c)
print("1-step(1c_30n LSTM RMSE :", RMSE30c)
MSE31c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test31c'])
RMSE31c = math.sqrt(MSE31c)
print("1-step(1c_31n LSTM RMSE :", RMSE31c)
MSE32c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test32c'])
RMSE32c = math.sqrt(MSE32c)
print("1-step(1c_32n LSTM RMSE :", RMSE32c)
MSE33c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test33c'])
RMSE33c = math.sqrt(MSE33c)
print("1-step(1c_33n LSTM RMSE :", RMSE33c)
MSE34c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test34c'])
RMSE34c = math.sqrt(MSE34c)
print("1-step(1c_34n LSTM RMSE :", RMSE34c)
MSE35c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test35c'])
RMSE35c = math.sqrt(MSE35c)
print("1-step(1c_35n LSTM RMSE :", RMSE35c)
MSE36c = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test36c'])
RMSE36c = math.sqrt(MSE36c)
print("1-step(1c_36n LSTM RMSE :", RMSE36c)
1-step(1c_2n LSTM RMSE : 0.054847988851892585 1-step(1c_3n LSTM RMSE : 0.054950304882192824 1-step(1c_4n LSTM RMSE : 0.05428165701002526 1-step(1c_5n LSTM RMSE : 0.06726511476776646 1-step(1c_6n LSTM RMSE : 0.043195044023896845 1-step(1c_7n LSTM RMSE : 0.05103699548918868 1-step(1c_8n LSTM RMSE : 0.050136505724341966 1-step(1c_9n LSTM RMSE : 0.05973789429201176 1-step(1c_10n LSTM RMSE : 0.04926743856122415 1-step(1c_11n LSTM RMSE : 0.04564535565134793 1-step(1c_12n LSTM RMSE : 0.059671013665996045 1-step(1c_13n LSTM RMSE : 0.0513349593893547 1-step(1c_14n LSTM RMSE : 0.057299476755583945 1-step(1c_15n LSTM RMSE : 0.05188207996977909 1-step(1c_16n LSTM RMSE : 0.052177002586673515 1-step(1c_17n LSTM RMSE : 0.06332616605464571 1-step(1c_18n LSTM RMSE : 0.0637373024799496 1-step(1c_19n LSTM RMSE : 0.05048696590097307 1-step(1c_20n LSTM RMSE : 0.06492117608007922 1-step(1c_21n LSTM RMSE : 0.05658233616086021 1-step(1c_22n LSTM RMSE : 0.057929643902592094 1-step(1c_23n LSTM RMSE : 0.04663767748628141 1-step(1c_24n LSTM RMSE : 0.0672477894796257 1-step(1c_25n LSTM RMSE : 0.0642700069006082 1-step(1c_26n LSTM RMSE : 0.06309760774989509 1-step(1c_27n LSTM RMSE : 0.06792630378597003 1-step(1c_28n LSTM RMSE : 0.07509285377159378 1-step(1c_29n LSTM RMSE : 0.05818496616413571 1-step(1c_30n LSTM RMSE : 0.047419147103763985 1-step(1c_31n LSTM RMSE : 0.06857006945932222 1-step(1c_32n LSTM RMSE : 0.05081496867898652 1-step(1c_33n LSTM RMSE : 0.0791245241177921 1-step(1c_34n LSTM RMSE : 0.06253183919875495 1-step(1c_35n LSTM RMSE : 0.06515059897593396 1-step(1c_36n LSTM RMSE : 0.05773031177874542
#RMSE Y MSE LSTM 1 CAPA
MSE2d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test2d'])
RMSE2d = math.sqrt(MSE2d)
print("1-step(2c_2n LSTM RMSE:", RMSE2d)
MSE3d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test3d'])
RMSE3d = math.sqrt(MSE3d)
print("1-step(2c_3n LSTM RMSE:", RMSE3d)
MSE4d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test4d'])
RMSE4d = math.sqrt(MSE4d)
print("1-step(2c_4n LSTM RMSE:", RMSE4d)
MSE5d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test5d'])
RMSE5d = math.sqrt(MSE5d)
print("1-step(2c_5n LSTM RMSE:", RMSE5d)
MSE6d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test6d'])
RMSE6d = math.sqrt(MSE6d)
print("1-step(2c_6n LSTM RMSE:", RMSE6d)
MSE7d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test7d'])
RMSE7d = math.sqrt(MSE7d)
print("1-step(2c_7n LSTM RMSE:", RMSE7d)
MSE8d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test8d'])
RMSE8d = math.sqrt(MSE8d)
print("1-step(2c_8n LSTM RMSE:", RMSE8d)
MSE9d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test9d'])
RMSE9d = math.sqrt(MSE9d)
print("1-step(2c_9n LSTM RMSE:", RMSE9d)
MSE10d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test10d'])
RMSE10d = math.sqrt(MSE10d)
print("1-step(2c_10n LSTM RMSE:", RMSE10d)
MSE11d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test11d'])
RMSE11d = math.sqrt(MSE11d)
print("1-step(2c_11n LSTM RMSE:", RMSE11d)
MSE12d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test12d'])
RMSE12d = math.sqrt(MSE12d)
print("1-step(2c_12n LSTM RMSE:", RMSE12d)
MSE13d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test13d'])
RMSE13d = math.sqrt(MSE13d)
print("1-step(2c_13n LSTM RMSE:", RMSE13d)
MSE14d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test14d'])
RMSE14d = math.sqrt(MSE14d)
print("1-step(2c_13n LSTM RMSE:", RMSE14d)
MSE15d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test15d'])
RMSE15d = math.sqrt(MSE15d)
print("1-step(2c_15n LSTM RMSE:", RMSE15d)
MSE16d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test16d'])
RMSE16d = math.sqrt(MSE16d)
print("1-step(2c_16n LSTM RMSE:", RMSE16d)
MSE17d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test17d'])
RMSE17d = math.sqrt(MSE17d)
print("1-step(2c_17n LSTM RMSE:", RMSE17d)
MSE18d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test18d'])
RMSE18d = math.sqrt(MSE18d)
print("1-step(2c_18n LSTM RMSE:", RMSE18d)
MSE19d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test19d'])
RMSE19d = math.sqrt(MSE19d)
print("1-step(2c_19n LSTM RMSE:", RMSE19d)
MSE20d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test20d'])
RMSE20d = math.sqrt(MSE20d)
print("1-step(2c_20n LSTM RMSE:", RMSE20d)
MSE21d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test21d'])
RMSE21d = math.sqrt(MSE21d)
print("1-step(2c_21n LSTM RMSE:", RMSE21d)
MSE22d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test22d'])
RMSE22d = math.sqrt(MSE22d)
print("1-step(2c_22n LSTM RMSE:", RMSE22d)
MSE23d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test23d'])
RMSE23d = math.sqrt(MSE23d)
print("1-step(2c_23n LSTM RMSE:", RMSE23d)
MSE24d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test24d'])
RMSE24d = math.sqrt(MSE24d)
print("1-step(2c_24n LSTM RMSE:", RMSE24d)
MSE25d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test25d'])
RMSE25d = math.sqrt(MSE25d)
print("1-step(2c_25n LSTM RMSE:", RMSE25d)
MSE26d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test26d'])
RMSE26d = math.sqrt(MSE26d)
print("1-step(2c_26n LSTM RMSE:", RMSE26d)
MSE27d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test27d'])
RMSE27d = math.sqrt(MSE27d)
print("1-step(2c_27n LSTM RMSE:", RMSE27d)
MSE28d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test28d'])
RMSE28d = math.sqrt(MSE28d)
print("1-step(2c_28n LSTM RMSE:", RMSE28d)
MSE29d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test29d'])
RMSE29d = math.sqrt(MSE29d)
print("1-step(2c_29n LSTM RMSE:", RMSE29d)
MSE30d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test30d'])
RMSE30d = math.sqrt(MSE30d)
print("1-step(2c_30n LSTM RMSE:", RMSE30d)
MSE31d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test31d'])
RMSE31d = math.sqrt(MSE31d)
print("1-step(2c_31n LSTM RMSE:", RMSE31d)
MSE32d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test32d'])
RMSE32d = math.sqrt(MSE32d)
print("1-step(2c_32n LSTM RMSE:", RMSE32d)
MSE33d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test33d'])
RMSE33d = math.sqrt(MSE33d)
print("1-step(2c_33n LSTM RMSE:", RMSE33d)
MSE34d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test34d'])
RMSE34d = math.sqrt(MSE34d)
print("1-step(2c_34n LSTM RMSE:", RMSE34d)
MSE35d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test35d'])
RMSE35d = math.sqrt(MSE35d)
print("1-step(2c_35n LSTM RMSE:", RMSE35d)
MSE36d = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test36d'])
RMSE36d = math.sqrt(MSE36d)
print("1-step(2c_36n LSTM RMSE:", RMSE36d)
1-step(2c_2n LSTM RMSE: 0.04280946198930851 1-step(2c_3n LSTM RMSE: 0.0427665925132219 1-step(2c_4n LSTM RMSE: 0.003205085008634539 1-step(2c_5n LSTM RMSE: 0.004760158886117657 1-step(2c_6n LSTM RMSE: 0.03919936265086284 1-step(2c_7n LSTM RMSE: 0.0450998908444205 1-step(2c_8n LSTM RMSE: 0.05804323700167221 1-step(2c_9n LSTM RMSE: 0.0547881478065852 1-step(2c_10n LSTM RMSE: 0.07237753552186965 1-step(2c_11n LSTM RMSE: 0.060253044848526255 1-step(2c_12n LSTM RMSE: 0.0515308345296485 1-step(2c_13n LSTM RMSE: 0.06575757069768053 1-step(2c_13n LSTM RMSE: 0.05057308513680855 1-step(2c_15n LSTM RMSE: 0.05778735127396485 1-step(2c_16n LSTM RMSE: 0.06238933325953529 1-step(2c_17n LSTM RMSE: 0.07127471753801569 1-step(2c_18n LSTM RMSE: 0.0620851084338445 1-step(2c_19n LSTM RMSE: 0.07124680607375633 1-step(2c_20n LSTM RMSE: 0.07510715332491695 1-step(2c_21n LSTM RMSE: 0.057434743238411266 1-step(2c_22n LSTM RMSE: 0.07355600510040598 1-step(2c_23n LSTM RMSE: 0.08166985647663753 1-step(2c_24n LSTM RMSE: 0.07621016904213947 1-step(2c_25n LSTM RMSE: 0.059725756143554666 1-step(2c_26n LSTM RMSE: 0.05853836557183695 1-step(2c_27n LSTM RMSE: 0.07089897154893439 1-step(2c_28n LSTM RMSE: 0.06897590168450705 1-step(2c_29n LSTM RMSE: 0.06215226055392902 1-step(2c_30n LSTM RMSE: 0.07053856944700541 1-step(2c_31n LSTM RMSE: 0.07244242616040256 1-step(2c_32n LSTM RMSE: 0.07907515235940216 1-step(2c_33n LSTM RMSE: 0.07960820920667307 1-step(2c_34n LSTM RMSE: 0.0630135471073688 1-step(2c_35n LSTM RMSE: 0.08315213223169642 1-step(2c_36n LSTM RMSE: 0.07679373069187181
#RMSE Y MSE BI-LSTM 1 CAPA
MSE2e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test2e'])
RMSE2e = math.sqrt(MSE2e)
print("1-step(1c_2n BI-LSTM RMSE:", RMSE2e)
MSE3e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test3e'])
RMSE3e = math.sqrt(MSE3e)
print("1-step(1c_3n BI-LSTM RMSE:", RMSE3e)
MSE4e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test4e'])
RMSE4e = math.sqrt(MSE4e)
print("1-step(1c_4n BI-LSTM RMSE:", RMSE4e)
MSE5e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test5e'])
RMSE5e = math.sqrt(MSE5e)
print("1-step(1c_5n BI-LSTM RMSE:", RMSE5e)
MSE6e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test6e'])
RMSE6e = math.sqrt(MSE6e)
print("1-step(1c_6n BI-LSTM RMSE:", RMSE6e)
MSE7e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test7e'])
RMSE7e = math.sqrt(MSE7e)
print("1-step(1c_7n BI-LSTM RMSE:", RMSE7e)
MSE8e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test8e'])
RMSE8e = math.sqrt(MSE8e)
print("1-step(1c_8n BI-LSTM RMSE:", RMSE8e)
MSE9e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test9e'])
RMSE9e = math.sqrt(MSE9e)
print("1-step(1c_9n BI-LSTM RMSE:", RMSE9e)
MSE10e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test10e'])
RMSE10e = math.sqrt(MSE10e)
print("1-step(1c_10n BI-LSTM RMSE:", RMSE10e)
MSE11e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test11e'])
RMSE11e = math.sqrt(MSE11e)
print("1-step(1c_11n BI-LSTM RMSE:", RMSE11e)
MSE12e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test12e'])
RMSE12e = math.sqrt(MSE12e)
print("1-step(1c_12n BI-LSTM RMSE:", RMSE12e)
MSE13e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test13e'])
RMSE13e = math.sqrt(MSE13e)
print("1-step(1c_13n BI-LSTM RMSE:", RMSE13e)
MSE14e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test14e'])
RMSE14e = math.sqrt(MSE14e)
print("1-step(1c_13n BI-LSTM RMSE:", RMSE14e)
MSE15e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test15e'])
RMSE15e = math.sqrt(MSE15e)
print("1-step(1c_15n BI-LSTM RMSE:", RMSE15e)
MSE16e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test16e'])
RMSE16e = math.sqrt(MSE16e)
print("1-step(1c_16n BI-LSTM RMSE:", RMSE16e)
MSE17e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test17e'])
RMSE17e = math.sqrt(MSE17e)
print("1-step(1c_17n BI-LSTM RMSE:", RMSE17e)
MSE18e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test18e'])
RMSE18e = math.sqrt(MSE18e)
print("1-step(1c_18n BI-LSTM RMSE:", RMSE18e)
MSE19e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test19e'])
RMSE19e = math.sqrt(MSE19e)
print("1-step(1c_19n BI-LSTM RMSE:", RMSE19e)
MSE20e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test20e'])
RMSE20e = math.sqrt(MSE20e)
print("1-step(1c_20n BI-LSTM RMSE:", RMSE20e)
MSE21e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test21e'])
RMSE21e = math.sqrt(MSE21e)
print("1-step(1c_21n BI-LSTM RMSE:", RMSE21e)
MSE22e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test22e'])
RMSE22e = math.sqrt(MSE22e)
print("1-step(1c_22n BI-LSTM RMSE:", RMSE22e)
MSE23e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test23e'])
RMSE23e = math.sqrt(MSE23e)
print("1-step(1c_23n BI-LSTM RMSE:", RMSE23e)
MSE24e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test24e'])
RMSE24e = math.sqrt(MSE24e)
print("1-step(1c_24n BI-LSTM RMSE:", RMSE24e)
MSE25e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test25e'])
RMSE25e = math.sqrt(MSE25e)
print("1-step(1c_25n BI-LSTM RMSE:", RMSE25e)
MSE26e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test26e'])
RMSE26e = math.sqrt(MSE26e)
print("1-step(1c_26n BI-LSTM RMSE:", RMSE26e)
MSE27e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test27e'])
RMSE27e = math.sqrt(MSE27e)
print("1-step(1c_27n BI-LSTM RMSE:", RMSE27e)
MSE28e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test28e'])
RMSE28e = math.sqrt(MSE28e)
print("1-step(1c_28n BI-LSTM RMSE:", RMSE28e)
MSE29e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test29e'])
RMSE29e = math.sqrt(MSE29e)
print("1-step(1c_29n BI-LSTM RMSE:", RMSE29e)
MSE30e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test30e'])
RMSE30e = math.sqrt(MSE30e)
print("1-step(1c_30n BI-LSTM RMSE:", RMSE30e)
MSE31e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test31e'])
RMSE31e = math.sqrt(MSE31e)
print("1-step(1c_31n BI-LSTM RMSE:", RMSE31e)
MSE32e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test32e'])
RMSE32e = math.sqrt(MSE32e)
print("1-step(1c_32n BI-LSTM RMSE:", RMSE32e)
MSE33e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test33e'])
RMSE33e = math.sqrt(MSE33e)
print("1-step(1c_33n BI-LSTM RMSE:", RMSE33e)
MSE34e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test34e'])
RMSE34e = math.sqrt(MSE34e)
print("1-step(1c_34n BI-LSTM RMSE:", RMSE34e)
MSE35e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test35e'])
RMSE35e = math.sqrt(MSE35e)
print("1-step(1c_35n BI-LSTM RMSE:", RMSE35e)
MSE36e = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test36e'])
RMSE36e = math.sqrt(MSE36e)
print("1-step(1c_36n BI-LSTM RMSE:", RMSE36e)
1-step(1c_2n BI-LSTM RMSE: 0.04181719436327279 1-step(1c_3n BI-LSTM RMSE: 0.04181369411129492 1-step(1c_4n BI-LSTM RMSE: 0.05356625373353308 1-step(1c_5n BI-LSTM RMSE: 0.06456835387396026 1-step(1c_6n BI-LSTM RMSE: 0.05890041243450532 1-step(1c_7n BI-LSTM RMSE: 0.05875233215370158 1-step(1c_8n BI-LSTM RMSE: 0.05334913193248797 1-step(1c_9n BI-LSTM RMSE: 0.05590668981412423 1-step(1c_10n BI-LSTM RMSE: 0.06353823695502685 1-step(1c_11n BI-LSTM RMSE: 0.06076792142821388 1-step(1c_12n BI-LSTM RMSE: 0.06421169670166932 1-step(1c_13n BI-LSTM RMSE: 0.06996788972426435 1-step(1c_13n BI-LSTM RMSE: 0.05864617063246759 1-step(1c_15n BI-LSTM RMSE: 0.06273350084629332 1-step(1c_16n BI-LSTM RMSE: 0.059331897010452474 1-step(1c_17n BI-LSTM RMSE: 0.05588401400903336 1-step(1c_18n BI-LSTM RMSE: 0.06809051113273994 1-step(1c_19n BI-LSTM RMSE: 0.07606735608099553 1-step(1c_20n BI-LSTM RMSE: 0.06895666269811053 1-step(1c_21n BI-LSTM RMSE: 0.0785560224202571 1-step(1c_22n BI-LSTM RMSE: 0.06824591502330332 1-step(1c_23n BI-LSTM RMSE: 0.07482329211826638 1-step(1c_24n BI-LSTM RMSE: 0.06949211366145955 1-step(1c_25n BI-LSTM RMSE: 0.056674140422909226 1-step(1c_26n BI-LSTM RMSE: 0.06720571169576724 1-step(1c_27n BI-LSTM RMSE: 0.074691820697476 1-step(1c_28n BI-LSTM RMSE: 0.07619473762661717 1-step(1c_29n BI-LSTM RMSE: 0.09836969739411335 1-step(1c_30n BI-LSTM RMSE: 0.06303370466975247 1-step(1c_31n BI-LSTM RMSE: 0.08049253316871048 1-step(1c_32n BI-LSTM RMSE: 0.07693042696051772 1-step(1c_33n BI-LSTM RMSE: 0.06419288506787241 1-step(1c_34n BI-LSTM RMSE: 0.0969035266178786 1-step(1c_35n BI-LSTM RMSE: 0.08862088047342488 1-step(1c_36n BI-LSTM RMSE: 0.08666083936644044
#RMSE Y MSE BI-LSTM 2 CAPAs
MSE2f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test2f'])
RMSE2f = math.sqrt(MSE2f)
print("1-step(2c_2n BI-LSTM RMSE:", RMSE2f)
MSE3f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test3f'])
RMSE3f = math.sqrt(MSE3f)
print("1-step(2c_3n BI-LSTM RMSE:", RMSE3f)
MSE4f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test4f'])
RMSE4f = math.sqrt(MSE4f)
print("1-step(2c_4n BI-LSTM RMSE:", RMSE4f)
MSE5f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test5f'])
RMSE5f = math.sqrt(MSE5f)
print("1-step(2c_5n BI-LSTM RMSE:", RMSE5f)
MSE6f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test6f'])
RMSE6f = math.sqrt(MSE6f)
print("1-step(2c_6n BI-LSTM RMSE:", RMSE6f)
MSE7f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test7f'])
RMSE7f = math.sqrt(MSE7f)
print("1-step(2c_7n BI-LSTM RMSE:", RMSE7f)
MSE8f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test8f'])
RMSE8f = math.sqrt(MSE8f)
print("1-step(2c_8n BI-LSTM RMSE:", RMSE8f)
MSE9f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test9f'])
RMSE9f = math.sqrt(MSE9f)
print("1-step(2c_9n BI-LSTM RMSE:", RMSE9f)
MSE10f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test10f'])
RMSE10f = math.sqrt(MSE10f)
print("1-step(2c_10n BI-LSTM RMSE:", RMSE10f)
MSE11f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test11f'])
RMSE11f = math.sqrt(MSE11f)
print("1-step(2c_11n BI-LSTM RMSE:", RMSE11f)
MSE12f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test12f'])
RMSE12f = math.sqrt(MSE12f)
print("1-step(2c_12n BI-LSTM RMSE:", RMSE12f)
MSE13f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test13f'])
RMSE13f = math.sqrt(MSE13f)
print("1-step(2c_13n BI-LSTM RMSE:", RMSE13f)
MSE14f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test14f'])
RMSE14f = math.sqrt(MSE14f)
print("1-step(2c_13n BI-LSTM RMSE:", RMSE14f)
MSE15f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test15f'])
RMSE15f = math.sqrt(MSE15f)
print("1-step(2c_15n BI-LSTM RMSE:", RMSE15f)
MSE16f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test16f'])
RMSE16f = math.sqrt(MSE16f)
print("1-step(2c_16n BI-LSTM RMSE:", RMSE16f)
MSE17f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test17f'])
RMSE17f = math.sqrt(MSE17f)
print("1-step(2c_17n BI-LSTM RMSE:", RMSE17f)
MSE18f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test18f'])
RMSE18f = math.sqrt(MSE18f)
print("1-step(2c_18n BI-LSTM RMSE:", RMSE18f)
MSE19f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test19f'])
RMSE19f = math.sqrt(MSE19f)
print("1-step(2c_19n BI-LSTM RMSE:", RMSE19f)
MSE20f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test20f'])
RMSE20f = math.sqrt(MSE20f)
print("1-step(2c_20n BI-LSTM RMSE:", RMSE20f)
MSE21f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test21f'])
RMSE21f = math.sqrt(MSE21f)
print("1-step(2c_21n BI-LSTM RMSE:", RMSE21f)
MSE22f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test22f'])
RMSE22f = math.sqrt(MSE22f)
print("1-step(2c_22n BI-LSTM RMSE:", RMSE22f)
MSE23f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test23f'])
RMSE23f = math.sqrt(MSE23f)
print("1-step(2c_23n BI-LSTM RMSE:", RMSE23f)
MSE24f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test24f'])
RMSE24f = math.sqrt(MSE24f)
print("1-step(2c_24n BI-LSTM RMSE:", RMSE24f)
MSE25f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test25f'])
RMSE25f = math.sqrt(MSE25f)
print("1-step(2c_25n BI-LSTM RMSE:", RMSE25f)
MSE26f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test26f'])
RMSE26f = math.sqrt(MSE26f)
print("1-step(2c_26n BI-LSTM RMSE:", RMSE26f)
MSE27f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test27f'])
RMSE27f = math.sqrt(MSE27f)
print("1-step(2c_27n BI-LSTM RMSE:", RMSE27f)
MSE28f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test28f'])
RMSE28f = math.sqrt(MSE28f)
print("1-step(2c_28n BI-LSTM RMSE:", RMSE28f)
MSE29f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test29f'])
RMSE29f = math.sqrt(MSE29f)
print("1-step(2c_29n BI-LSTM RMSE:", RMSE29f)
MSE30f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test30f'])
RMSE30f = math.sqrt(MSE30f)
print("1-step(2c_30n BI-LSTM RMSE:", RMSE30f)
MSE31f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test31f'])
RMSE31f = math.sqrt(MSE31f)
print("1-step(2c_31n BI-LSTM RMSE:", RMSE31f)
MSE32f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test32f'])
RMSE32f = math.sqrt(MSE32f)
print("1-step(2c_32n BI-LSTM RMSE:", RMSE32f)
MSE33f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test33f'])
RMSE33f = math.sqrt(MSE33f)
print("1-step(2c_33n BI-LSTM RMSE:", RMSE33f)
MSE34f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test34f'])
RMSE34f = math.sqrt(MSE34f)
print("1-step(2c_34n BI-LSTM RMSE:", RMSE34f)
MSE35f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test35f'])
RMSE35f = math.sqrt(MSE35f)
print("1-step(2c_35n BI-LSTM RMSE:", RMSE35f)
MSE36f = mean_squared_error(test_log_pass, df.loc[test_idx, '1step_test36f'])
RMSE36f = math.sqrt(MSE36f)
print("1-step(2c_36n BI-LSTM RMSE:", RMSE36f)
1-step(2c_2n BI-LSTM RMSE: 0.05943081557165116 1-step(2c_3n BI-LSTM RMSE: 0.059545548442823684 1-step(2c_4n BI-LSTM RMSE: 0.06082237655918341 1-step(2c_5n BI-LSTM RMSE: 0.05852272045925626 1-step(2c_6n BI-LSTM RMSE: 0.05807834279642043 1-step(2c_7n BI-LSTM RMSE: 0.055214200843328023 1-step(2c_8n BI-LSTM RMSE: 0.05573644535701958 1-step(2c_9n BI-LSTM RMSE: 0.057633425006234903 1-step(2c_10n BI-LSTM RMSE: 0.053551289972761165 1-step(2c_11n BI-LSTM RMSE: 0.06714258769026994 1-step(2c_12n BI-LSTM RMSE: 0.0675781847603187 1-step(2c_13n BI-LSTM RMSE: 0.06445588455874088 1-step(2c_13n BI-LSTM RMSE: 0.07223134219959808 1-step(2c_15n BI-LSTM RMSE: 0.0650106158002643 1-step(2c_16n BI-LSTM RMSE: 0.060139967474128066 1-step(2c_17n BI-LSTM RMSE: 0.062140028064692744 1-step(2c_18n BI-LSTM RMSE: 0.08377833935263283 1-step(2c_19n BI-LSTM RMSE: 0.06484707357576443 1-step(2c_20n BI-LSTM RMSE: 0.07286200738487514 1-step(2c_21n BI-LSTM RMSE: 0.06843036000976645 1-step(2c_22n BI-LSTM RMSE: 0.06941348939984213 1-step(2c_23n BI-LSTM RMSE: 0.055180235048498595 1-step(2c_24n BI-LSTM RMSE: 0.054117428020998434 1-step(2c_25n BI-LSTM RMSE: 0.05690079108618626 1-step(2c_26n BI-LSTM RMSE: 0.05153009091101996 1-step(2c_27n BI-LSTM RMSE: 0.04435016005909553 1-step(2c_28n BI-LSTM RMSE: 0.04359129305429019 1-step(2c_29n BI-LSTM RMSE: 0.0655586982370667 1-step(2c_30n BI-LSTM RMSE: 0.0561790026639208 1-step(2c_31n BI-LSTM RMSE: 0.05449209815634237 1-step(2c_32n BI-LSTM RMSE: 0.0498712702364843 1-step(2c_33n BI-LSTM RMSE: 0.0329079938119688 1-step(2c_34n BI-LSTM RMSE: 0.06939985446578331 1-step(2c_35n BI-LSTM RMSE: 0.032946301131950156 1-step(2c_36n BI-LSTM RMSE: 0.06385108574340936
# Agregamos los mape
mape_1c_rnn = [mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10, mape11, mape12, mape13, mape14, mape15, mape16, mape17, mape18, mape19, mape20, mape21, mape22, mape23, mape24, mape25, mape26, mape27, mape28, mape29, mape30, mape31, mape32, mape33, mape34, mape35, mape36]
mape_2c_rnn = [mape2b, mape3b, mape4b, mape5b, mape6b, mape7b, mape8b, mape9b, mape10b, mape11b, mape12b, mape13b, mape14b, mape15b, mape16b, mape17b, mape18b, mape19b, mape20b, mape21b, mape22b, mape23b, mape24b, mape25b, mape26b, mape27b, mape28b, mape29b, mape30b, mape31b, mape32b, mape33b, mape34b, mape35b, mape36b]
mape_1c_lstm = [mape2c, mape3c, mape4c, mape5c, mape6c, mape7c, mape8c, mape9c, mape10c, mape11c, mape12c, mape13c, mape14c, mape15c, mape16c, mape17c, mape18c, mape19c, mape20c, mape21c, mape22c, mape23c, mape24c, mape25c, mape26c, mape27c, mape28c, mape29c, mape30c, mape31c, mape32c, mape33c, mape34c, mape35c, mape36c]
mape_2c_lstm = [mape2d, mape3d, mape4d, mape5d, mape6d, mape7d, mape8d, mape9d, mape10d, mape11d, mape12d, mape13d, mape14d, mape15d, mape16d, mape17d, mape18d, mape19d, mape20d, mape21d, mape22d, mape23d, mape24d, mape25d, mape26d, mape27d, mape28d, mape29d, mape30d, mape31d, mape32d, mape33d, mape34d, mape35d, mape36d]
mape_1c_bilstm = [mape2e, mape3e, mape4e, mape5e, mape6e, mape7e, mape8e, mape9e, mape10e, mape11e, mape12e, mape13e, mape14e, mape15e, mape16e, mape17e, mape18e, mape19e, mape20e, mape21e, mape22e, mape23e, mape24e, mape25e, mape26e, mape27e, mape28e, mape29e, mape30e, mape31e, mape32e, mape33e, mape34e, mape35e, mape36e]
mape_2c_bilstm = [mape2f, mape3f, mape4f, mape5f, mape6f, mape7f, mape8f, mape9f, mape10f, mape11f, mape12f, mape13f, mape14f, mape15f, mape16f, mape17f, mape18f, mape19f, mape20f, mape21f, mape22f, mape23f, mape24f, mape25f, mape26f, mape27f, mape28f, mape29f, mape30f, mape31f, mape32f, mape33f, mape34f, mape35f, mape36f]
# Agregamos los RMSE
RMSE_1c_rnn = [RMSE2, RMSE3, RMSE4, RMSE5, RMSE6, RMSE7, RMSE8, RMSE9, RMSE10, RMSE11, RMSE12, RMSE13, RMSE14, RMSE15, RMSE16, RMSE17, RMSE18, RMSE19, RMSE20, RMSE21, RMSE22, RMSE23, RMSE24, RMSE25, RMSE26, RMSE27, RMSE28, RMSE29, RMSE30, RMSE31, RMSE32, RMSE33, RMSE34, RMSE35, RMSE36]
RMSE_2c_rnn = [RMSE2b, RMSE3b, RMSE4b, RMSE5b, RMSE6b, RMSE7b, RMSE8b, RMSE9b, RMSE10b, RMSE11b, RMSE12b, RMSE13b, RMSE14b, RMSE15b, RMSE16b, RMSE17b, RMSE18b, RMSE19b, RMSE20b, RMSE21b, RMSE22b, RMSE23b, RMSE24b, RMSE25b, RMSE26b, RMSE27b, RMSE28b, RMSE29b, RMSE30b, RMSE31b, RMSE32b, RMSE33b, RMSE34b, RMSE35b, RMSE36b]
RMSE_1c_lstm = [RMSE2c, RMSE3c, RMSE4c, RMSE5c, RMSE6c, RMSE7c, RMSE8c, RMSE9c, RMSE10c, RMSE11c, RMSE12c, RMSE13c, RMSE14c, RMSE15c, RMSE16c, RMSE17c, RMSE18c, RMSE19c, RMSE20c, RMSE21c, RMSE22c, RMSE23c, RMSE24c, RMSE25c, RMSE26c, RMSE27c, RMSE28c, RMSE29c, RMSE30c, RMSE31c, RMSE32c, RMSE33c, RMSE34c, RMSE35c, RMSE36c]
RMSE_2c_lstm = [RMSE2d, RMSE3d, RMSE4d, RMSE5d, RMSE6d, RMSE7d, RMSE8d, RMSE9d, RMSE10d, RMSE11d, RMSE12d, RMSE13d, RMSE14d, RMSE15d, RMSE16d, RMSE17d, RMSE18d, RMSE19d, RMSE20d, RMSE21d, RMSE22d, RMSE23d, RMSE24d, RMSE25d, RMSE26d, RMSE27d, RMSE28d, RMSE29d, RMSE30d, RMSE31d, RMSE32d, RMSE33d, RMSE34d, RMSE35d, RMSE36d]
RMSE_1c_bilstm = [RMSE2e, RMSE3e, RMSE4e, RMSE5e, RMSE6e, RMSE7e, RMSE8e, RMSE9e, RMSE10e, RMSE11e, RMSE12e, RMSE13e, RMSE14e, RMSE15e, RMSE16e, RMSE17e, RMSE18e, RMSE19e, RMSE20e, RMSE21e, RMSE22e, RMSE23e, RMSE24e, RMSE25e, RMSE26e, RMSE27e, RMSE28e, RMSE29e, RMSE30e, RMSE31e, RMSE32e, RMSE33e, RMSE34e, RMSE35e, RMSE36e]
RMSE_2c_bilstm = [RMSE2f, RMSE3f, RMSE4f, RMSE5f, RMSE6f, RMSE7f, RMSE8f, RMSE9f, RMSE10f, RMSE11f, RMSE12f, RMSE13f, RMSE14f, RMSE15f, RMSE16f, RMSE17f, RMSE18f, RMSE19f, RMSE20f, RMSE21f, RMSE22f, RMSE23f, RMSE24f, RMSE25f, RMSE26f, RMSE27f, RMSE28f, RMSE29f, RMSE30f, RMSE31f, RMSE32f, RMSE33f, RMSE34f, RMSE35f, RMSE36f]
#Los metemos en un DataFrame para exportarlos a excel en caso de necesidad:
RMSE_df_1c_rnn = pd.DataFrame(data = RMSE_1c_rnn)
RMSE_df_1c_rnn.index =['RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9','RMSE10','RMSE11','RMSE12','RMSE13','RMSE14','RMSE15','RMSE16','RMSE17','RMSE18','RMSE19','RMSE20','RMSE21','RMSE22','RMSE23','RMSE24','RMSE25','RMSE26','RMSE27','RMSE28','RMSE29','RMSE30','RMSE31','RMSE32','RMSE33','RMSE34','RMSE35','RMSE36']
RMSE_df_2c_rnn = pd.DataFrame(data = RMSE_2c_rnn)
RMSE_df_2c_rnn.index =['RMSE2b', 'RMSE3b', 'RMSE4b', 'RMSE5b', 'RMSE6b', 'RMSE7b', 'RMSE8b', 'RMSE9b','RMSE10b','RMSE11b','RMSE12b','RMSE13b','RMSE14b','RMSE15b','RMSE16b','RMSE17b','RMSE18b','RMSE19b','RMSE20b','RMSE21b','RMSE22b','RMSE23b','RMSE24b','RMSE25b','RMSE26b','RMSE27b','RMSE28b','RMSE29b','RMSE30b','RMSE31b','RMSE32b','RMSE33b','RMSE34b','RMSE35b','RMSE36b']
RMSE_df_1c_lstm = pd.DataFrame(data = RMSE_1c_lstm)
RMSE_df_1c_lstm.index =['RMSE2c', 'RMSE3c', 'RMSE4c', 'RMSE5c', 'RMSE6c', 'RMSE7c', 'RMSE8c', 'RMSE9c','RMSE10c','RMSE11c','RMSE12c','RMSE13c','RMSE14c','RMSE15c','RMSE16c','RMSE17c','RMSE18c','RMSE19c','RMSE20c','RMSE21c','RMSE22c','RMSE23c','RMSE24c','RMSE25c','RMSE26c','RMSE27c','RMSE28c','RMSE29c','RMSE30c','RMSE31c','RMSE32c','RMSE33c','RMSE34c','RMSE35c','RMSE36c']
RMSE_df_2c_lstm = pd.DataFrame(data = RMSE_2c_lstm)
RMSE_df_2c_lstm.index =['RMSE2d', 'RMSE3d', 'RMSE4d', 'RMSE5d', 'RMSE6d', 'RMSE7d', 'RMSE8d', 'RMSE9d','RMSE10d','RMSE11d','RMSE12d','RMSE13d','RMSE14d','RMSE15d','RMSE16d','RMSE17d','RMSE18d','RMSE19d','RMSE20d','RMSE21d','RMSE22d','RMSE23d','RMSE24d','RMSE25d','RMSE26d','RMSE27d','RMSE28d','RMSE29d','RMSE30d','RMSE31d','RMSE32d','RMSE33d','RMSE34d','RMSE35d','RMSE36d']
RMSE_df_1c_bilstm = pd.DataFrame(data = RMSE_1c_bilstm)
RMSE_df_1c_bilstm.index =['RMSE2e', 'RMSE3e', 'RMSE4e', 'RMSE5e', 'RMSE6e', 'RMSE7e', 'RMSE8e', 'RMSE9e','RMSE10e','RMSE11e','RMSE12e','RMSE13e','RMSE14e','RMSE15e','RMSE16e','RMSE17e','RMSE18e','RMSE19e','RMSE20e','RMSE21e','RMSE22e','RMSE23e','RMSE24e','RMSE25e','RMSE26e','RMSE27e','RMSE28e','RMSE29e','RMSE30e','RMSE31d','RMSE32e','RMSE33e','RMSE34e','RMSE35e','RMSE36e']
RMSE_df_2c_bilstm = pd.DataFrame(data = RMSE_2c_bilstm)
RMSE_df_2c_bilstm.index =['RMSE2f', 'RMSE3f', 'RMSE4f', 'RMSE5f', 'RMSE6f', 'RMSE7f', 'RMSE8f', 'RMSE9f','RMSE10f','RMSE11f','RMSE12f','RMSE13f','RMSE14f','RMSE15f','RMSE16f','RMSE17f','RMSE18f','RMSE19f','RMSE20f','RMSE21f','RMSE22f','RMSE23f','RMSE24f','RMSE25f','RMSE26f','RMSE27f','RMSE28f','RMSE29f','RMSE30f','RMSE31f','RMSE32f','RMSE33f','RMSE34f','RMSE35f','RMSE36f']
RMSE_df_1c_rnn.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\RMSE\RMSE_df_1c_rnn.xlsx', index = False, header=True)
RMSE_df_2c_rnn.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\RMSE\RMSE_df_2c_rnn.xlsx', index = False, header=True)
RMSE_df_1c_lstm.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\RMSE\RMSE_df_1c_lstm.xlsx', index = False, header=True)
RMSE_df_2c_lstm.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\RMSE\RMSE_df_2c_lstm.xlsx', index = False, header=True)
RMSE_df_1c_bilstm.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\RMSE\RMSE_df_1c_bilstm.xlsx', index = False, header=True)
RMSE_df_2c_bilstm.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\RMSE\RMSE_df_2c_bilstm.xlsx', index = False, header=True)
#Mejores modelos por MAPE
#Los metemos en un DataFrame para exportarlos a excel en caso de necesidad:
MAPE_df_1c_rnn = pd.DataFrame(data = mape_1c_rnn)
MAPE_df_1c_rnn.index =['MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9','MAPE10','MAPE11','MAPE12','MAPE13','MAPE14','MAPE15','MAPE16','MAPE17','MAPE18','MAPE19','MAPE20','MAPE21','MAPE22','MAPE23','MAPE24','MAPE25','MAPE26','MAPE27','MAPE28','MAPE29','MAPE30','MAPE31','MAPE32','MAPE33','MAPE34','MAPE35','MAPE36']
MAPE_df_2c_rnn = pd.DataFrame(data = mape_2c_rnn)
MAPE_df_2c_rnn.index =['MAPE2b', 'MAPE3b', 'MAPE4b', 'MAPE5b', 'MAPE6b', 'MAPE7b', 'MAPE8b', 'MAPE9b','MAPE10b','MAPE11b','MAPE12b','MAPE13b','MAPE14b','MAPE15b','MAPE16b','MAPE17b','MAPE18b','MAPE19b','MAPE20b','MAPE21b','MAPE22b','MAPE23b','MAPE24b','MAPE25b','MAPE26b','MAPE27b','MAPE28b','MAPE29b','MAPE30b','MAPE31b','MAPE32b','MAPE33b','MAPE34b','MAPE35b','MAPE36b']
MAPE_df_1c_lstm = pd.DataFrame(data = mape_1c_lstm)
MAPE_df_1c_lstm.index =['MAPE2c', 'MAPE3c', 'MAPE4c', 'MAPE5c', 'MAPE6c', 'MAPE7c', 'MAPE8c', 'MAPE9c','MAPE10c','MAPE11c','MAPE12c','MAPE13c','MAPE14c','MAPE15c','MAPE16c','MAPE17c','MAPE18c','MAPE19c','MAPE20c','MAPE21c','MAPE22c','MAPE23c','MAPE24c','MAPE25c','MAPE26c','MAPE27c','MAPE28c','MAPE29c','MAPE30c','MAPE31c','MAPE32c','MAPE33c','MAPE34c','MAPE35c','MAPE36c']
MAPE_df_2c_lstm = pd.DataFrame(data = mape_2c_lstm)
MAPE_df_2c_lstm.index =['MAPE2d', 'MAPE3d', 'MAPE4d', 'MAPE5d', 'MAPE6d', 'MAPE7d', 'MAPE8d', 'MAPE9d','MAPE10d','MAPE11d','MAPE12d','MAPE13d','MAPE14d','MAPE15d','MAPE16d','MAPE17d','MAPE18d','MAPE19d','MAPE20d','MAPE21d','MAPE22d','MAPE23d','MAPE24d','MAPE25d','MAPE26d','MAPE27d','MAPE28d','MAPE29d','MAPE30d','MAPE31d','MAPE32d','MAPE33d','MAPE34d','MAPE35d','MAPE36d']
MAPE_df_1c_bilstm = pd.DataFrame(data = mape_1c_bilstm)
MAPE_df_1c_bilstm.index =['MAPE2e', 'MAPE3e', 'MAPE4e', 'MAPE5e', 'MAPE6e', 'MAPE7e', 'MAPE8e', 'MAPE9e','MAPE10e','MAPE11e','MAPE12e','MAPE13e','MAPE14e','MAPE15e','MAPE16e','MAPE17e','MAPE18e','MAPE19e','MAPE20e','MAPE21e','MAPE22e','MAPE23e','MAPE24e','MAPE25e','MAPE26e','MAPE27e','MAPE28e','MAPE29e','MAPE30e','MAPE31d','MAPE32e','MAPE33e','MAPE34e','MAPE35e','MAPE36e']
MAPE_df_2c_bilstm = pd.DataFrame(data = mape_2c_bilstm)
MAPE_df_2c_bilstm.index =['MAPE2f', 'MAPE3f', 'MAPE4f', 'MAPE5f', 'MAPE6f', 'MAPE7f', 'MAPE8f', 'MAPE9f','MAPE10f','MAPE11f','MAPE12f','MAPE13f','MAPE14f','MAPE15f','MAPE16f','MAPE17f','MAPE18f','MAPE19f','MAPE20f','MAPE21f','MAPE22f','MAPE23f','MAPE24f','MAPE25f','MAPE26f','MAPE27f','MAPE28f','MAPE29f','MAPE30f','MAPE31f','MAPE32f','MAPE33f','MAPE34f','MAPE35f','MAPE36f']
MAPE_df_1c_rnn.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\MAPE\MAPE_df_1c_rnn.xlsx', index = False, header=True)
MAPE_df_2c_rnn.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\MAPE\MAPE_df_2c_rnn.xlsx', index = False, header=True)
MAPE_df_1c_lstm.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\MAPE\MAPE_df_1c_lstm.xlsx', index = False, header=True)
MAPE_df_2c_lstm.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\MAPE\MAPE_df_2c_lstm.xlsx', index = False, header=True)
MAPE_df_1c_bilstm.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\MAPE\MAPE_df_1c_bilstm.xlsx', index = False, header=True)
MAPE_df_2c_bilstm.to_excel (r'C:\Users\Scarl\Dropbox\Maestría ITESM\Tesina\Modelos\Definitivos\completo\Revision\MAPE\MAPE_df_2c_bilstm.xlsx', index = False, header=True)
Mejores modelos por MAPE. No se está usando este criterio en el trabajo pero no está de mas tenerlo.
best_1c_rnn_MAPE = MAPE_df_1c_rnn.idxmin(axis=0)[0]
print("Mejor RNN de 1 capa con MAPE:", MAPE_df_1c_rnn.idxmin(axis=0)[0],MAPE_df_1c_rnn[0].min())
Mejor RNN de 1 capa con MAPE: MAPE4 0.00208995914353786
best_2c_rnn_MAPE = MAPE_df_2c_rnn.idxmin(axis=0)[0]
print("Mejor RNN de 2 capas con MAPE:", MAPE_df_2c_rnn.idxmin(axis=0)[0],MAPE_df_2c_rnn[0].min())
Mejor RNN de 2 capas con MAPE: MAPE19b 0.000785666839120728
best_1c_lstm_MAPE = MAPE_df_1c_lstm.idxmin(axis=0)[0]
print("Mejor LSTM de 1 capa con MAPE:", MAPE_df_1c_lstm.idxmin(axis=0)[0],MAPE_df_1c_lstm[0].min())
Mejor LSTM de 1 capa con MAPE: MAPE6c 0.010985117756850795
best_2c_lstm_MAPE = MAPE_df_2c_lstm.idxmin(axis=0)[0]
print("Mejor LSTM de 2 capas con MAPE:", MAPE_df_2c_lstm.idxmin(axis=0)[0],MAPE_df_2c_lstm[0].min())
Mejor LSTM de 2 capas con MAPE: MAPE4d 0.0002169355629570848
best_1c_bilstm_MAPE = MAPE_df_1c_bilstm.idxmin(axis=0)[0]
print("Mejor BI-LSTM de 1 capa con MAPE:", MAPE_df_1c_bilstm.idxmin(axis=0)[0],MAPE_df_1c_bilstm[0].min())
Mejor BI-LSTM de 1 capa con MAPE: MAPE3e 0.01063234348256671
best_2c_bilstm_MAPE = MAPE_df_2c_bilstm.idxmin(axis=0)[0]
print("Mejor BI-LSTM de 2 capa con MAPE:", MAPE_df_2c_rnn.idxmin(axis=0),MAPE_df_2c_rnn[0].min())
Mejor BI-LSTM de 2 capa con MAPE: 0 MAPE19b dtype: object 0.000785666839120728
Mejores modelos por RMSE. No se está usando este criterio en el trabaoj pero no está de mas tenerlo.
best_1c_rnn = RMSE_df_1c_rnn.idxmin(axis=0)[0]
print("Mejor RNN de 1 capa:", RMSE_df_1c_rnn.idxmin(axis=0)[0],RMSE_df_1c_rnn[0].min())
Mejor RNN de 1 capa: RMSE4 0.008328520583599806
best_2c_rnn = RMSE_df_2c_rnn.idxmin(axis=0)[0]
print("Mejor RNN de 2 capas:", RMSE_df_2c_rnn.idxmin(axis=0)[0],RMSE_df_2c_rnn[0].min())
Mejor RNN de 2 capas: RMSE19b 0.004024418256100607
best_1c_lstm = RMSE_df_1c_lstm.idxmin(axis=0)[0]
print("Mejor LSTM de 1 capa:", RMSE_df_1c_lstm.idxmin(axis=0)[0],RMSE_df_1c_lstm[0].min())
Mejor LSTM de 1 capa: RMSE6c 0.043195044023896845
best_2c_lstm = RMSE_df_2c_lstm.idxmin(axis=0)[0]
print("Mejor LSTM de 2 capas:", RMSE_df_2c_lstm.idxmin(axis=0)[0],RMSE_df_2c_lstm[0].min())
Mejor LSTM de 2 capas: RMSE4d 0.003205085008634539
best_1c_bilstm = RMSE_df_1c_bilstm.idxmin(axis=0)[0]
print("Mejor BI-LSTM de 1 capa:", RMSE_df_1c_bilstm.idxmin(axis=0)[0],RMSE_df_1c_bilstm[0].min())
Mejor BI-LSTM de 1 capa: RMSE3e 0.04181369411129492
best_2c_bilstm = RMSE_df_2c_bilstm.idxmin(axis=0)[0]
print("Mejor BI-LSTM de 2 capas:", RMSE_df_2c_bilstm.idxmin(axis=0)[0],RMSE_df_2c_bilstm[0].min())
Mejor BI-LSTM de 2 capas: RMSE33f 0.0329079938119688
#RNN 1 Capa
# serialize model to JSON
model2_json = model2.to_json()
with open("model2.json", "w") as json_file:
json_file.write(model2_json)
# serialize weights to HDF5
model2.save_weights("model2.h5")
model3_json = model3.to_json()
with open("model3.json", "w") as json_file:
json_file.write(model3_json)
model3.save_weights("model3.h5")
model4_json = model4.to_json()
with open("model4.json", "w") as json_file:
json_file.write(model4_json)
model4.save_weights("model4.h5")
model5_json = model5.to_json()
with open("model5.json", "w") as json_file:
json_file.write(model5_json)
model5.save_weights("model5.h5")
model6_json = model6.to_json()
with open("model6.json", "w") as json_file:
json_file.write(model6_json)
model6.save_weights("model6.h5")
model7_json = model7.to_json()
with open("model7.json", "w") as json_file:
json_file.write(model7_json)
model7.save_weights("model7.h5")
model8_json = model8.to_json()
with open("model8.json", "w") as json_file:
json_file.write(model8_json)
model8.save_weights("model8.h5")
model9_json = model9.to_json()
with open("model9.json", "w") as json_file:
json_file.write(model9_json)
model9.save_weights("model9.h5")
model10_json = model10.to_json()
with open("model10.json", "w") as json_file:
json_file.write(model10_json)
model10.save_weights("model10.h5")
model11_json = model11.to_json()
with open("model11.json", "w") as json_file:
json_file.write(model11_json)
model11.save_weights("model11.h5")
model12_json = model12.to_json()
with open("model12.json", "w") as json_file:
json_file.write(model12_json)
model12.save_weights("model12.h5")
model13_json = model3.to_json()
with open("model13.json", "w") as json_file:
json_file.write(model3_json)
model13.save_weights("model3.h5")
model14_json = model14.to_json()
with open("model14.json", "w") as json_file:
json_file.write(model14_json)
model14.save_weights("model14.h5")
model15_json = model15.to_json()
with open("model15.json", "w") as json_file:
json_file.write(model15_json)
model15.save_weights("model15.h5")
model16_json = model16.to_json()
with open("model16.json", "w") as json_file:
json_file.write(model16_json)
model16.save_weights("model16.h5")
model17_json = model17.to_json()
with open("model17.json", "w") as json_file:
json_file.write(model17_json)
model17.save_weights("model17.h5")
model18_json = model18.to_json()
with open("model18.json", "w") as json_file:
json_file.write(model18_json)
model18.save_weights("model18.h5")
model19_json = model19.to_json()
with open("model19.json", "w") as json_file:
json_file.write(model19_json)
model19.save_weights("model19.h5")
model20_json = model20.to_json()
with open("model20.json", "w") as json_file:
json_file.write(model20_json)
model20.save_weights("model20.h5")
model21_json = model21.to_json()
with open("model21.json", "w") as json_file:
json_file.write(model21_json)
model21.save_weights("model21.h5")
model22_json = model22.to_json()
with open("model22.json", "w") as json_file:
json_file.write(model22_json)
model22.save_weights("model22.h5")
model23_json = model23.to_json()
with open("model23.json", "w") as json_file:
json_file.write(model23_json)
model23.save_weights("model23.h5")
model24_json = model24.to_json()
with open("model24.json", "w") as json_file:
json_file.write(model24_json)
model24.save_weights("model24.h5")
model25_json = model25.to_json()
with open("model25.json", "w") as json_file:
json_file.write(model25_json)
model25.save_weights("model25.h5")
model26_json = model26.to_json()
with open("model26.json", "w") as json_file:
json_file.write(model26_json)
model26.save_weights("model26.h5")
model27_json = model27.to_json()
with open("model27.json", "w") as json_file:
json_file.write(model27_json)
model27.save_weights("model27.h5")
model28_json = model28.to_json()
with open("model28.json", "w") as json_file:
json_file.write(model28_json)
model28.save_weights("model28.h5")
model29_json = model29.to_json()
with open("model29.json", "w") as json_file:
json_file.write(model29_json)
model29.save_weights("model29.h5")
model30_json = model30.to_json()
with open("model30.json", "w") as json_file:
json_file.write(model30_json)
model30.save_weights("model30.h5")
model31_json = model31.to_json()
with open("model31.json", "w") as json_file:
json_file.write(model31_json)
model31.save_weights("model31.h5")
model32_json = model32.to_json()
with open("model32.json", "w") as json_file:
json_file.write(model32_json)
model32.save_weights("model32.h5")
model33_json = model33.to_json()
with open("model33.json", "w") as json_file:
json_file.write(model33_json)
model33.save_weights("model33.h5")
model34_json = model34.to_json()
with open("model34.json", "w") as json_file:
json_file.write(model34_json)
model34.save_weights("model34.h5")
model35_json = model35.to_json()
with open("model35.json", "w") as json_file:
json_file.write(model35_json)
model35.save_weights("model35.h5")
model36_json = model36.to_json()
with open("model36.json", "w") as json_file:
json_file.write(model36_json)
model36.save_weights("model36.h5")
#RNN 2 Capa
model2b_json = model2b.to_json()
with open("model2b.json", "w") as json_file:
json_file.write(model2b_json)
model2b.save_weights("model2b.h5")
model3b_json = model3b.to_json()
with open("model3b.json", "w") as json_file:
json_file.write(model3b_json)
model3b.save_weights("model3b.h5")
model4b_json = model4b.to_json()
with open("model4b.json", "w") as json_file:
json_file.write(model4b_json)
model4b.save_weights("model4b.h5")
model5b_json = model5b.to_json()
with open("model5b.json", "w") as json_file:
json_file.write(model5b_json)
model5b.save_weights("model5b.h5")
model6b_json = model6b.to_json()
with open("model6b.json", "w") as json_file:
json_file.write(model6b_json)
model6b.save_weights("model6b.h5")
model7b_json = model7b.to_json()
with open("model7b.json", "w") as json_file:
json_file.write(model7b_json)
model7b.save_weights("model7b.h5")
model8b_json = model8b.to_json()
with open("model8b.json", "w") as json_file:
json_file.write(model8b_json)
model8b.save_weights("model8b.h5")
model9b_json = model9b.to_json()
with open("model9b.json", "w") as json_file:
json_file.write(model9b_json)
model9b.save_weights("model9b.h5")
model10b_json = model10b.to_json()
with open("model10b.json", "w") as json_file:
json_file.write(model10b_json)
model10b.save_weights("model10b.h5")
model11b_json = model11b.to_json()
with open("model11b.json", "w") as json_file:
json_file.write(model11b_json)
model11b.save_weights("model11b.h5")
model12b_json = model12b.to_json()
with open("model12b.json", "w") as json_file:
json_file.write(model12b_json)
model12b.save_weights("model12b.h5")
model13b_json = model3b.to_json()
with open("model13b.json", "w") as json_file:
json_file.write(model3b_json)
model13b.save_weights("model3b.h5")
model14b_json = model14b.to_json()
with open("model14b.json", "w") as json_file:
json_file.write(model14b_json)
model14b.save_weights("model14b.h5")
model15b_json = model15b.to_json()
with open("model15b.json", "w") as json_file:
json_file.write(model15b_json)
model15b.save_weights("model15b.h5")
model16b_json = model16b.to_json()
with open("model16b.json", "w") as json_file:
json_file.write(model16b_json)
model16b.save_weights("model16b.h5")
model17b_json = model17b.to_json()
with open("model17b.json", "w") as json_file:
json_file.write(model17b_json)
model17b.save_weights("model17b.h5")
model18b_json = model18b.to_json()
with open("model18b.json", "w") as json_file:
json_file.write(model18b_json)
model18b.save_weights("model18b.h5")
model19b_json = model19b.to_json()
with open("model19b.json", "w") as json_file:
json_file.write(model19b_json)
model19b.save_weights("model19b.h5")
model20b_json = model20b.to_json()
with open("model20b.json", "w") as json_file:
json_file.write(model20b_json)
model20b.save_weights("model20b.h5")
model21b_json = model21b.to_json()
with open("model21b.json", "w") as json_file:
json_file.write(model21b_json)
model21b.save_weights("model21b.h5")
model22b_json = model22b.to_json()
with open("model22b.json", "w") as json_file:
json_file.write(model22b_json)
model22b.save_weights("model22b.h5")
model23b_json = model23b.to_json()
with open("model23b.json", "w") as json_file:
json_file.write(model23b_json)
model23b.save_weights("model23b.h5")
model24b_json = model24b.to_json()
with open("model24b.json", "w") as json_file:
json_file.write(model24b_json)
model24b.save_weights("model24b.h5")
model25b_json = model25b.to_json()
with open("model25b.json", "w") as json_file:
json_file.write(model25b_json)
model25b.save_weights("model25b.h5")
model26b_json = model26b.to_json()
with open("model26b.json", "w") as json_file:
json_file.write(model26b_json)
model26b.save_weights("model26b.h5")
model27b_json = model27b.to_json()
with open("model27b.json", "w") as json_file:
json_file.write(model27b_json)
model27b.save_weights("model27b.h5")
model28b_json = model28b.to_json()
with open("model28b.json", "w") as json_file:
json_file.write(model28b_json)
model28b.save_weights("model28b.h5")
model29b_json = model29b.to_json()
with open("model29b.json", "w") as json_file:
json_file.write(model29b_json)
model29b.save_weights("model29b.h5")
model30b_json = model30b.to_json()
with open("model30b.json", "w") as json_file:
json_file.write(model30b_json)
model30b.save_weights("model30b.h5")
model31b_json = model31b.to_json()
with open("model31b.json", "w") as json_file:
json_file.write(model31b_json)
model31b.save_weights("model31b.h5")
model32b_json = model32b.to_json()
with open("model32b.json", "w") as json_file:
json_file.write(model32b_json)
model32b.save_weights("model32b.h5")
model33b_json = model33b.to_json()
with open("model33b.json", "w") as json_file:
json_file.write(model33b_json)
model33b.save_weights("model33b.h5")
model34b_json = model34b.to_json()
with open("model34b.json", "w") as json_file:
json_file.write(model34b_json)
model34b.save_weights("model34b.h5")
model35b_json = model35b.to_json()
with open("model35b.json", "w") as json_file:
json_file.write(model35b_json)
model35b.save_weights("model35b.h5")
model36b_json = model36b.to_json()
with open("model36b.json", "w") as json_file:
json_file.write(model36b_json)
model36b.save_weights("model36b.h5")
#LSTM 1 Capa
model2c_json = model2c.to_json()
with open("model2c.json", "w") as json_file:
json_file.write(model2c_json)
model2c.save_weights("model2c.h5")
model3c_json = model3c.to_json()
with open("model3c.json", "w") as json_file:
json_file.write(model3c_json)
model3c.save_weights("model3c.h5")
model4c_json = model4c.to_json()
with open("model4c.json", "w") as json_file:
json_file.write(model4c_json)
model4c.save_weights("model4c.h5")
model5c_json = model5c.to_json()
with open("model5c.json", "w") as json_file:
json_file.write(model5c_json)
model5c.save_weights("model5c.h5")
model6c_json = model6c.to_json()
with open("model6c.json", "w") as json_file:
json_file.write(model6c_json)
model6c.save_weights("model6c.h5")
model7c_json = model7c.to_json()
with open("model7c.json", "w") as json_file:
json_file.write(model7c_json)
model7c.save_weights("model7c.h5")
model8c_json = model8c.to_json()
with open("model8c.json", "w") as json_file:
json_file.write(model8c_json)
model8c.save_weights("model8c.h5")
model9c_json = model9c.to_json()
with open("model9c.json", "w") as json_file:
json_file.write(model9c_json)
model9c.save_weights("model9c.h5")
model10c_json = model10c.to_json()
with open("model10c.json", "w") as json_file:
json_file.write(model10c_json)
model10c.save_weights("model10c.h5")
model11c_json = model11c.to_json()
with open("model11c.json", "w") as json_file:
json_file.write(model11c_json)
model11c.save_weights("model11c.h5")
model12c_json = model12c.to_json()
with open("model12c.json", "w") as json_file:
json_file.write(model12c_json)
model12c.save_weights("model12c.h5")
model13c_json = model3c.to_json()
with open("model13c.json", "w") as json_file:
json_file.write(model3c_json)
model13c.save_weights("model3c.h5")
model14c_json = model14c.to_json()
with open("model14c.json", "w") as json_file:
json_file.write(model14c_json)
model14c.save_weights("model14c.h5")
model15c_json = model15c.to_json()
with open("model15c.json", "w") as json_file:
json_file.write(model15c_json)
model15c.save_weights("model15c.h5")
model16c_json = model16c.to_json()
with open("model16c.json", "w") as json_file:
json_file.write(model16c_json)
model16c.save_weights("model16c.h5")
model17c_json = model17c.to_json()
with open("model17c.json", "w") as json_file:
json_file.write(model17c_json)
model17c.save_weights("model17c.h5")
model18c_json = model18c.to_json()
with open("model18c.json", "w") as json_file:
json_file.write(model18c_json)
model18c.save_weights("model18c.h5")
model19c_json = model19c.to_json()
with open("model19c.json", "w") as json_file:
json_file.write(model19c_json)
model19c.save_weights("model19c.h5")
model20c_json = model20c.to_json()
with open("model20c.json", "w") as json_file:
json_file.write(model20c_json)
model20c.save_weights("model20c.h5")
model21c_json = model21c.to_json()
with open("model21c.json", "w") as json_file:
json_file.write(model21c_json)
model21c.save_weights("model21c.h5")
model22c_json = model22c.to_json()
with open("model22c.json", "w") as json_file:
json_file.write(model22c_json)
model22c.save_weights("model22c.h5")
model23c_json = model23c.to_json()
with open("model23c.json", "w") as json_file:
json_file.write(model23c_json)
model23c.save_weights("model23c.h5")
model24c_json = model24c.to_json()
with open("model24c.json", "w") as json_file:
json_file.write(model24c_json)
model24c.save_weights("model24c.h5")
model25c_json = model25c.to_json()
with open("model25c.json", "w") as json_file:
json_file.write(model25c_json)
model25c.save_weights("model25c.h5")
model26c_json = model26c.to_json()
with open("model26c.json", "w") as json_file:
json_file.write(model26c_json)
model26c.save_weights("model26c.h5")
model27c_json = model27c.to_json()
with open("model27c.json", "w") as json_file:
json_file.write(model27c_json)
model27c.save_weights("model27c.h5")
model28c_json = model28c.to_json()
with open("model28c.json", "w") as json_file:
json_file.write(model28c_json)
model28c.save_weights("model28c.h5")
model29c_json = model29c.to_json()
with open("model29c.json", "w") as json_file:
json_file.write(model29c_json)
model29c.save_weights("model29c.h5")
model30c_json = model30c.to_json()
with open("model30c.json", "w") as json_file:
json_file.write(model30c_json)
model30c.save_weights("model30c.h5")
model31c_json = model31c.to_json()
with open("model31c.json", "w") as json_file:
json_file.write(model31c_json)
model31c.save_weights("model31c.h5")
model32c_json = model32c.to_json()
with open("model32c.json", "w") as json_file:
json_file.write(model32c_json)
model32c.save_weights("model32c.h5")
model33c_json = model33c.to_json()
with open("model33c.json", "w") as json_file:
json_file.write(model33c_json)
model33c.save_weights("model33c.h5")
model34c_json = model34c.to_json()
with open("model34c.json", "w") as json_file:
json_file.write(model34c_json)
model34c.save_weights("model34c.h5")
model35c_json = model35c.to_json()
with open("model35c.json", "w") as json_file:
json_file.write(model35c_json)
model35c.save_weights("model35c.h5")
model36c_json = model36c.to_json()
with open("model36c.json", "w") as json_file:
json_file.write(model36c_json)
model36c.save_weights("model36c.h5")
#LSTM 2 Capa
model2d_json = model2d.to_json()
with open("model2d.json", "w") as json_file:
json_file.write(model2d_json)
model2d.save_weights("model2d.h5")
model3d_json = model3d.to_json()
with open("model3d.json", "w") as json_file:
json_file.write(model3d_json)
model3d.save_weights("model3d.h5")
model4d_json = model4d.to_json()
with open("model4d.json", "w") as json_file:
json_file.write(model4d_json)
model4d.save_weights("model4d.h5")
model5d_json = model5d.to_json()
with open("model5d.json", "w") as json_file:
json_file.write(model5d_json)
model5d.save_weights("model5d.h5")
model6d_json = model6d.to_json()
with open("model6d.json", "w") as json_file:
json_file.write(model6d_json)
model6d.save_weights("model6d.h5")
model7d_json = model7d.to_json()
with open("model7d.json", "w") as json_file:
json_file.write(model7d_json)
model7d.save_weights("model7d.h5")
model8d_json = model8d.to_json()
with open("model8d.json", "w") as json_file:
json_file.write(model8d_json)
model8d.save_weights("model8d.h5")
model9d_json = model9d.to_json()
with open("model9d.json", "w") as json_file:
json_file.write(model9d_json)
model9d.save_weights("model9d.h5")
model10d_json = model10d.to_json()
with open("model10d.json", "w") as json_file:
json_file.write(model10d_json)
model10d.save_weights("model10d.h5")
model11d_json = model11d.to_json()
with open("model11d.json", "w") as json_file:
json_file.write(model11d_json)
model11d.save_weights("model11d.h5")
model12d_json = model12d.to_json()
with open("model12d.json", "w") as json_file:
json_file.write(model12d_json)
model12d.save_weights("model12d.h5")
model13d_json = model3d.to_json()
with open("model13d.json", "w") as json_file:
json_file.write(model3d_json)
model13d.save_weights("model3d.h5")
model14d_json = model14d.to_json()
with open("model14d.json", "w") as json_file:
json_file.write(model14d_json)
model14d.save_weights("model14d.h5")
model15d_json = model15d.to_json()
with open("model15d.json", "w") as json_file:
json_file.write(model15d_json)
model15d.save_weights("model15d.h5")
model16d_json = model16d.to_json()
with open("model16d.json", "w") as json_file:
json_file.write(model16d_json)
model16d.save_weights("model16d.h5")
model17d_json = model17d.to_json()
with open("model17d.json", "w") as json_file:
json_file.write(model17d_json)
model17d.save_weights("model17d.h5")
model18d_json = model18d.to_json()
with open("model18d.json", "w") as json_file:
json_file.write(model18d_json)
model18d.save_weights("model18d.h5")
model19d_json = model19d.to_json()
with open("model19d.json", "w") as json_file:
json_file.write(model19d_json)
model19d.save_weights("model19d.h5")
model20d_json = model20d.to_json()
with open("model20d.json", "w") as json_file:
json_file.write(model20d_json)
model20d.save_weights("model20d.h5")
model21d_json = model21d.to_json()
with open("model21d.json", "w") as json_file:
json_file.write(model21d_json)
model21d.save_weights("model21d.h5")
model22d_json = model22d.to_json()
with open("model22d.json", "w") as json_file:
json_file.write(model22d_json)
model22d.save_weights("model22d.h5")
model23d_json = model23d.to_json()
with open("model23d.json", "w") as json_file:
json_file.write(model23d_json)
model23d.save_weights("model23d.h5")
model24d_json = model24d.to_json()
with open("model24d.json", "w") as json_file:
json_file.write(model24d_json)
model24d.save_weights("model24d.h5")
model25d_json = model25d.to_json()
with open("model25d.json", "w") as json_file:
json_file.write(model25d_json)
model25d.save_weights("model25d.h5")
model26d_json = model26d.to_json()
with open("model26d.json", "w") as json_file:
json_file.write(model26d_json)
model26d.save_weights("model26d.h5")
model27d_json = model27d.to_json()
with open("model27d.json", "w") as json_file:
json_file.write(model27d_json)
model27d.save_weights("model27d.h5")
model28d_json = model28d.to_json()
with open("model28d.json", "w") as json_file:
json_file.write(model28d_json)
model28d.save_weights("model28d.h5")
model29d_json = model29d.to_json()
with open("model29d.json", "w") as json_file:
json_file.write(model29d_json)
model29d.save_weights("model29d.h5")
model30d_json = model30d.to_json()
with open("model30d.json", "w") as json_file:
json_file.write(model30d_json)
model30d.save_weights("model30d.h5")
model31d_json = model31d.to_json()
with open("model31d.json", "w") as json_file:
json_file.write(model31d_json)
model31d.save_weights("model31d.h5")
model32d_json = model32d.to_json()
with open("model32d.json", "w") as json_file:
json_file.write(model32d_json)
model32d.save_weights("model32d.h5")
model33d_json = model33d.to_json()
with open("model33d.json", "w") as json_file:
json_file.write(model33d_json)
model33d.save_weights("model33d.h5")
model34d_json = model34d.to_json()
with open("model34d.json", "w") as json_file:
json_file.write(model34d_json)
model34d.save_weights("model34d.h5")
model35d_json = model35d.to_json()
with open("model35d.json", "w") as json_file:
json_file.write(model35d_json)
model35d.save_weights("model35d.h5")
model36d_json = model36d.to_json()
with open("model36d.json", "w") as json_file:
json_file.write(model36d_json)
model36d.save_weights("model36d.h5")
#BI-LSTM 1 Capa
model2e_json = model2e.to_json()
with open("model2e.json", "w") as json_file:
json_file.write(model2e_json)
model2e.save_weights("model2e.h5")
model3e_json = model3e.to_json()
with open("model3e.json", "w") as json_file:
json_file.write(model3e_json)
model3e.save_weights("model3e.h5")
model4e_json = model4e.to_json()
with open("model4e.json", "w") as json_file:
json_file.write(model4e_json)
model4e.save_weights("model4e.h5")
model5e_json = model5e.to_json()
with open("model5e.json", "w") as json_file:
json_file.write(model5e_json)
model5e.save_weights("model5e.h5")
model6e_json = model6e.to_json()
with open("model6e.json", "w") as json_file:
json_file.write(model6e_json)
model6e.save_weights("model6e.h5")
model7e_json = model7e.to_json()
with open("model7e.json", "w") as json_file:
json_file.write(model7e_json)
model7e.save_weights("model7e.h5")
model8e_json = model8e.to_json()
with open("model8e.json", "w") as json_file:
json_file.write(model8e_json)
model8e.save_weights("model8e.h5")
model9e_json = model9e.to_json()
with open("model9e.json", "w") as json_file:
json_file.write(model9e_json)
model9e.save_weights("model9e.h5")
model10e_json = model10e.to_json()
with open("model10e.json", "w") as json_file:
json_file.write(model10e_json)
model10e.save_weights("model10e.h5")
model11e_json = model11e.to_json()
with open("model11e.json", "w") as json_file:
json_file.write(model11e_json)
model11e.save_weights("model11e.h5")
model12e_json = model12e.to_json()
with open("model12e.json", "w") as json_file:
json_file.write(model12e_json)
model12e.save_weights("model12e.h5")
model13e_json = model3e.to_json()
with open("model13e.json", "w") as json_file:
json_file.write(model3e_json)
model13e.save_weights("model3e.h5")
model14e_json = model14e.to_json()
with open("model14e.json", "w") as json_file:
json_file.write(model14e_json)
model14e.save_weights("model14e.h5")
model15e_json = model15e.to_json()
with open("model15e.json", "w") as json_file:
json_file.write(model15e_json)
model15e.save_weights("model15e.h5")
model16e_json = model16e.to_json()
with open("model16e.json", "w") as json_file:
json_file.write(model16e_json)
model16e.save_weights("model16e.h5")
model17e_json = model17e.to_json()
with open("model17e.json", "w") as json_file:
json_file.write(model17e_json)
model17e.save_weights("model17e.h5")
model18e_json = model18e.to_json()
with open("model18e.json", "w") as json_file:
json_file.write(model18e_json)
model18e.save_weights("model18e.h5")
model19e_json = model19e.to_json()
with open("model19e.json", "w") as json_file:
json_file.write(model19e_json)
model19e.save_weights("model19e.h5")
model20e_json = model20e.to_json()
with open("model20e.json", "w") as json_file:
json_file.write(model20e_json)
model20e.save_weights("model20e.h5")
model21e_json = model21e.to_json()
with open("model21e.json", "w") as json_file:
json_file.write(model21e_json)
model21e.save_weights("model21e.h5")
model22e_json = model22e.to_json()
with open("model22e.json", "w") as json_file:
json_file.write(model22e_json)
model22e.save_weights("model22e.h5")
model23e_json = model23e.to_json()
with open("model23e.json", "w") as json_file:
json_file.write(model23e_json)
model23e.save_weights("model23e.h5")
model24e_json = model24e.to_json()
with open("model24e.json", "w") as json_file:
json_file.write(model24e_json)
model24e.save_weights("model24e.h5")
model25e_json = model25e.to_json()
with open("model25e.json", "w") as json_file:
json_file.write(model25e_json)
model25e.save_weights("model25e.h5")
model26e_json = model26e.to_json()
with open("model26e.json", "w") as json_file:
json_file.write(model26e_json)
model26e.save_weights("model26e.h5")
model27e_json = model27e.to_json()
with open("model27e.json", "w") as json_file:
json_file.write(model27e_json)
model27e.save_weights("model27e.h5")
model28e_json = model28e.to_json()
with open("model28e.json", "w") as json_file:
json_file.write(model28e_json)
model28e.save_weights("model28e.h5")
model29e_json = model29e.to_json()
with open("model29e.json", "w") as json_file:
json_file.write(model29e_json)
model29e.save_weights("model29e.h5")
model30e_json = model30e.to_json()
with open("model30e.json", "w") as json_file:
json_file.write(model30e_json)
model30e.save_weights("model30e.h5")
model31e_json = model31e.to_json()
with open("model31e.json", "w") as json_file:
json_file.write(model31e_json)
model31e.save_weights("model31e.h5")
model32e_json = model32e.to_json()
with open("model32e.json", "w") as json_file:
json_file.write(model32e_json)
model32e.save_weights("model32e.h5")
model33e_json = model33e.to_json()
with open("model33e.json", "w") as json_file:
json_file.write(model33e_json)
model33e.save_weights("model33e.h5")
model34e_json = model34e.to_json()
with open("model34e.json", "w") as json_file:
json_file.write(model34e_json)
model34e.save_weights("model34e.h5")
model35e_json = model35e.to_json()
with open("model35e.json", "w") as json_file:
json_file.write(model35e_json)
model35e.save_weights("model35e.h5")
model36e_json = model36e.to_json()
with open("model36e.json", "w") as json_file:
json_file.write(model36e_json)
model36e.save_weights("model36e.h5")
#BI-LSTM 2 Capa
model2f_json = model2f.to_json()
with open("model2f.json", "w") as json_file:
json_file.write(model2f_json)
model2f.save_weights("model2f.h5")
model3f_json = model3f.to_json()
with open("model3f.json", "w") as json_file:
json_file.write(model3f_json)
model3f.save_weights("model3f.h5")
model4f_json = model4f.to_json()
with open("model4f.json", "w") as json_file:
json_file.write(model4f_json)
model4f.save_weights("model4f.h5")
model5f_json = model5f.to_json()
with open("model5f.json", "w") as json_file:
json_file.write(model5f_json)
model5f.save_weights("model5f.h5")
model6f_json = model6f.to_json()
with open("model6f.json", "w") as json_file:
json_file.write(model6f_json)
model6f.save_weights("model6f.h5")
model7f_json = model7f.to_json()
with open("model7f.json", "w") as json_file:
json_file.write(model7f_json)
model7f.save_weights("model7f.h5")
model8f_json = model8f.to_json()
with open("model8f.json", "w") as json_file:
json_file.write(model8f_json)
model8f.save_weights("model8f.h5")
model9f_json = model9f.to_json()
with open("model9f.json", "w") as json_file:
json_file.write(model9f_json)
model9f.save_weights("model9f.h5")
model10f_json = model10f.to_json()
with open("model10f.json", "w") as json_file:
json_file.write(model10f_json)
model10f.save_weights("model10f.h5")
model11f_json = model11f.to_json()
with open("model11f.json", "w") as json_file:
json_file.write(model11f_json)
model11f.save_weights("model11f.h5")
model12f_json = model12f.to_json()
with open("model12f.json", "w") as json_file:
json_file.write(model12f_json)
model12f.save_weights("model12f.h5")
model13f_json = model3f.to_json()
with open("model13f.json", "w") as json_file:
json_file.write(model3f_json)
model13f.save_weights("model3f.h5")
model14f_json = model14f.to_json()
with open("model14f.json", "w") as json_file:
json_file.write(model14f_json)
model14f.save_weights("model14f.h5")
model15f_json = model15f.to_json()
with open("model15f.json", "w") as json_file:
json_file.write(model15f_json)
model15f.save_weights("model15f.h5")
model16f_json = model16f.to_json()
with open("model16f.json", "w") as json_file:
json_file.write(model16f_json)
model16f.save_weights("model16f.h5")
model17f_json = model17f.to_json()
with open("model17f.json", "w") as json_file:
json_file.write(model17f_json)
model17f.save_weights("model17f.h5")
model18f_json = model18f.to_json()
with open("model18f.json", "w") as json_file:
json_file.write(model18f_json)
model18f.save_weights("model18f.h5")
model19f_json = model19f.to_json()
with open("model19f.json", "w") as json_file:
json_file.write(model19f_json)
model19f.save_weights("model19f.h5")
model20f_json = model20f.to_json()
with open("model20f.json", "w") as json_file:
json_file.write(model20f_json)
model20f.save_weights("model20f.h5")
model21f_json = model21f.to_json()
with open("model21f.json", "w") as json_file:
json_file.write(model21f_json)
model21f.save_weights("model21f.h5")
model22f_json = model22f.to_json()
with open("model22f.json", "w") as json_file:
json_file.write(model22f_json)
model22f.save_weights("model22f.h5")
model23f_json = model23f.to_json()
with open("model23f.json", "w") as json_file:
json_file.write(model23f_json)
model23f.save_weights("model23f.h5")
model24f_json = model24f.to_json()
with open("model24f.json", "w") as json_file:
json_file.write(model24f_json)
model24f.save_weights("model24f.h5")
model25f_json = model25f.to_json()
with open("model25f.json", "w") as json_file:
json_file.write(model25f_json)
model25f.save_weights("model25f.h5")
model26f_json = model26f.to_json()
with open("model26f.json", "w") as json_file:
json_file.write(model26f_json)
model26f.save_weights("model26f.h5")
model27f_json = model27f.to_json()
with open("model27f.json", "w") as json_file:
json_file.write(model27f_json)
model27f.save_weights("model27f.h5")
model28f_json = model28f.to_json()
with open("model28f.json", "w") as json_file:
json_file.write(model28f_json)
model28f.save_weights("model28f.h5")
model29f_json = model29f.to_json()
with open("model29f.json", "w") as json_file:
json_file.write(model29f_json)
model29f.save_weights("model29f.h5")
model30f_json = model30f.to_json()
with open("model30f.json", "w") as json_file:
json_file.write(model30f_json)
model30f.save_weights("model30f.h5")
model31f_json = model31f.to_json()
with open("model31f.json", "w") as json_file:
json_file.write(model31f_json)
model31f.save_weights("model31f.h5")
model32f_json = model32f.to_json()
with open("model32f.json", "w") as json_file:
json_file.write(model32f_json)
model32f.save_weights("model32f.h5")
model33f_json = model33f.to_json()
with open("model33f.json", "w") as json_file:
json_file.write(model33f_json)
model33f.save_weights("model33f.h5")
model34f_json = model34f.to_json()
with open("model34f.json", "w") as json_file:
json_file.write(model34f_json)
model34f.save_weights("model34f.h5")
model35f_json = model35f.to_json()
with open("model35f.json", "w") as json_file:
json_file.write(model35f_json)
model35f.save_weights("model35f.h5")
model36f_json = model36f.to_json()
with open("model36f.json", "w") as json_file:
json_file.write(model36f_json)
model36f.save_weights("model36f.h5")
#Visualizamos los mejores modelos de cada tipo de Red Neuronal con RMSE
print('RNN 1 capa',best_1c_rnn)
print('RNN 2 capas',best_2c_rnn)
print('LSTM 1 capa',best_1c_lstm)
print('LSTM 2 capas',best_2c_lstm)
print('BI-LSTM 1 capa',best_1c_bilstm)
print('BI-LSTM 2 capas',best_2c_bilstm)
RNN 1 capa RMSE4 RNN 2 capas RMSE19b LSTM 1 capa RMSE6c LSTM 2 capas RMSE4d BI-LSTM 1 capa RMSE3e BI-LSTM 2 capas RMSE33f
#Visualizamos los mejores modelos de cada tipo de Red Neuronal con MAPE
print('RNN 1 capa',best_1c_rnn_MAPE)
print('RNN 2 capas',best_2c_rnn_MAPE)
print('LSTM 1 capa',best_1c_lstm_MAPE)
print('LSTM 2 capas',best_2c_lstm_MAPE)
print('BI-LSTM 1 capa',best_1c_bilstm_MAPE)
print('BI-LSTM 2 capas',best_2c_bilstm_MAPE)
RNN 1 capa MAPE4 RNN 2 capas MAPE19b LSTM 1 capa MAPE6c LSTM 2 capas MAPE4d BI-LSTM 1 capa MAPE3e BI-LSTM 2 capas MAPE33f
#Visualizamos los mejores modelos de cada tipo de Red Neuronal con MAPE
print('RNN 1 capa',best_1c_rnn_MAPE)
print('RNN 2 capas',best_2c_rnn_MAPE)
print('LSTM 1 capa',best_1c_lstm_MAPE)
print('LSTM 2 capas',best_2c_lstm_MAPE)
print('BI-LSTM 1 capa',best_1c_bilstm_MAPE)
print('BI-LSTM 2 capas',best_2c_bilstm_MAPE)
RNN 1 capa MAPE4 RNN 2 capas MAPE19b LSTM 1 capa MAPE6c LSTM 2 capas MAPE4d BI-LSTM 1 capa MAPE3e BI-LSTM 2 capas MAPE33f
########################## Sección donde recuperas los modelos guardados ##################################
from keras.models import model_from_json
#Cargar: Mejor modelo 1 capa RNN
json_file_4 = open('model4.json', 'r')
loaded_model4_json = json_file_4.read()
json_file.close()
model4 = model_from_json(loaded_model4_json)
model4.load_weights("model4.h5")
print("Loaded model from disk", best_1c_rnn)
Loaded model from disk RMSE4
#Cargar: Mejor modelo 2 capa RNN
json_file_23b = open('model23b.json', 'r')
loaded_model23b_json = json_file_23b.read()
json_file_23b.close()
model23b = model_from_json(loaded_model23b_json)
model23b.load_weights("model23b.h5")
print("Loaded model from disk", best_2c_rnn)
Loaded model from disk RMSE19b
#Cargar: Mejor modelo 1 capa LSTM
json_file_6c = open('model6c.json', 'r')
loaded_model6c_json = json_file_6c.read()
json_file_6c.close()
model6c = model_from_json(loaded_model6c_json)
model6c.load_weights("model6c.h5")
print("Loaded model from disk", best_1c_lstm)
Loaded model from disk RMSE6c
#Cargar: Mejor modelo 2 capa LSTM
json_file_4d = open('model4d.json', 'r')
loaded_model4d_json = json_file_4d.read()
json_file_4d.close()
model4d = model_from_json(loaded_model4d_json)
model4d.load_weights("model4d.h5")
print("Loaded model from disk", best_2c_lstm)
Loaded model from disk RMSE4d
#Cargar: Mejor modelo 1 capa BI-LSTM
json_file_4e = open('model4e.json', 'r')
loaded_model4e_json = json_file_4e.read()
json_file_4e.close()
model4e = model_from_json(loaded_model4e_json)
model4e.load_weights("model4e.h5")
print("Loaded model from disk", best_1c_bilstm)
Loaded model from disk RMSE3e
#Cargar: Mejor modelo 2 capa BI-LSTM
json_file_33f = open('model33f.json', 'r')
loaded_model33f_json = json_file_33f.read()
json_file_33f.close()
model33f = model_from_json(loaded_model33f_json)
model33f.load_weights("model33f.h5")
print("Loaded model from disk", best_2c_bilstm)
Loaded model from disk RMSE33f
#Gráficando con el mejor modelo usando RMSE
# plot a line graph
plt.plot(RMSE_df_1c_rnn[0], linestyle='dashed', marker ='o',markerfacecolor='blue',color='green')
plt.show()
plt.savefig('RMSE_df_1c_rnn.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(RMSE_df_2c_rnn[0], linestyle='dashed', marker ='o',markerfacecolor='blue',color='green')
plt.show()
plt.savefig('RMSE_df_2c_rnn.png')
plt.savefig('RMSE_df_2c_rnn.pdf')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(RMSE_df_2c_rnn[0], linestyle='dashed', marker ='o',markerfacecolor='blue',color='green')
plt.show()
plt.savefig('RMSE_df_2c_rnn.png')
plt.savefig('RMSE_df_2c_rnn.pdf')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(RMSE_df_1c_lstm[0], linestyle='dashed', marker ='o',markerfacecolor='orange',color='green')
plt.show()
plt.savefig('RMSE_df_1c_lstm.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(RMSE_df_2c_lstm[0], linestyle='dashed', marker ='o',markerfacecolor='red',color='green')
plt.show()
plt.savefig('RMSE_df_2c_lstm.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(RMSE_df_1c_bilstm[0], linestyle='dashed', marker ='o',markerfacecolor='purple',color='green')
plt.show()
plt.savefig('RMSE_df_1c_bilstm.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(RMSE_df_2c_bilstm[0], linestyle='dashed', marker ='o',markerfacecolor='yellow',color='green')
plt.show()
plt.savefig('RMSE_df_2c_bilstm.png')
<Figure size 432x288 with 0 Axes>
#Gráficando el mejor modelo usando MAPE
# plot a line graph
plt.plot(MAPE_df_1c_rnn[0], linestyle='dashed', marker ='o',markerfacecolor='blue',color='green')
plt.show()
plt.savefig('MAPE_df_1c_rnn.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(MAPE_df_2c_rnn[0], linestyle='dashed', marker ='o',markerfacecolor='blue',color='green')
plt.show()
plt.savefig('MAPE_df_2c_rnn.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(MAPE_df_1c_lstm[0], linestyle='dashed', marker ='o',markerfacecolor='orange',color='green')
plt.show()
plt.savefig('MAPE_df_1c_lstm.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(MAPE_df_2c_lstm[0], linestyle='dashed', marker ='o',markerfacecolor='red',color='green')
plt.show()
plt.savefig('MAPE_df_2c_lstm.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(MAPE_df_1c_bilstm[0], linestyle='dashed', marker ='o',markerfacecolor='purple',color='green')
plt.show()
plt.savefig('MAPE_df_1c_bilstm.png')
<Figure size 432x288 with 0 Axes>
# plot a line graph
plt.plot(MAPE_df_2c_bilstm[0], linestyle='dashed', marker ='o',markerfacecolor='yellow',color='green')
plt.show()
plt.savefig('MAPE_df_2c_bilstm.png')
<Figure size 432x288 with 0 Axes>